Automatic speech recognition and speech variability: A review

Pattern classification and scene analysis

Duda, R. O., & Hart, P. E. (1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895 · see citing works

Adaptive Filter Theory

Haykin, S. (1986) · Cited by 12,603 · see citing works

Introduction to Statistical Pattern Recognition

(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,056 · see citing works

Fundamentals of speech recognition

Rabiner, L. R., & Juang, B.-H. F. (1993) · Cited by 7,680 · see citing works

Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences

Davis, S., & Mermelstein, P. (1980) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1980.1163420 · Cited by 5,465 · see citing works

Linear prediction: A tutorial review

Makhoul, J. (1975) · Proceedings of the IEEE · doi:10.1109/proc.1975.9792 · Cited by 4,047 · see citing works

Introduction to statistical pattern recognition (2nd ed.)

Fukunaga, K. (1990) · Academic Press Professional, Inc. eBooks · Cited by 3,802 · see citing works

Entropy-based algorithms for best basis selection

Coifman, R. R., & Wickerhauser, M. V. (1992) · IEEE Transactions on Information Theory · doi:10.1109/18.119732 · Cited by 3,202 · see citing works

Adaptive filter theory

Wittenmark, B. (1993) · Automatica · doi:10.1016/0005-1098(93)90162-m · Cited by 3,067 · see citing works

Acoustic Theory of Speech Production

Fant, G. (1971) · De Gruyter eBooks · doi:10.1515/9783110873429 · Cited by 2,958 · see citing works

Control Methods Used in a Study of the Vowels

Peterson, G. E., & Barney, H. L. (1952) · The Journal of the Acoustical Society of America · doi:10.1121/1.1906875 · Cited by 2,837 · see citing works

Perceptual linear predictive (PLP) analysis of speech

Heřmanský, H. (1990) · The Journal of the Acoustical Society of America · doi:10.1121/1.399423 · Cited by 2,550 · see citing works

Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models

Leggetter, C. J., & Woodland, P. C. (1995) · Computer Speech & Language · doi:10.1006/csla.1995.0010 · Cited by 2,210 · see citing works

Vocal communication of emotion: A review of research paradigms

Scherer, K. (2003) · Speech Communication · doi:10.1016/s0167-6393(02)00084-5 · Cited by 2,044 · see citing works

Communication Systems

Haykin, S. (1978) · Cited by 1,827 · see citing works

Explaining Phonetic Variation: A Sketch of the H&H Theory

Lindblom, B. E. F. (1990) · Springer eBooks · doi:10.1007/978-94-009-2037-8_16 · Cited by 1,826 · see citing works

RASTA processing of speech

Heřmanský, H., & Morgan, N. (1994) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.326616 · Cited by 1,822 · see citing works

Principles of Phonetics

Laver, J. (1994) · doi:10.1017/cbo9781139166621 · Cited by 1,298 · see citing works

A post-processing system to yield reduced word error rates: Recognizer Output Voting Error Reduction (ROVER)

Fiscus, J. (2002) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.1997.659110 · Cited by 1,102 · see citing works

Toward the simulation of emotion in synthetic speech: A review of the literature on human vocal emotion

Murray, I. R., & Arnott, J. L. (1993) · The Journal of the Acoustical Society of America · doi:10.1121/1.405558 · Cited by 1,098 · see citing works

Pattern Classification and Scene Analysis.

Grenander, U., Duda, R. O., & Hart, P. E. (1974) · Journal of the American Statistical Association · doi:10.2307/2286028 · Cited by 1,018 · see citing works

Acoustics of children’s speech: Developmental changes of temporal and spectral parameters

Lee, S., Potamianos, A., & Narayanan, S. S. (1999) · The Journal of the Acoustical Society of America · doi:10.1121/1.426686 · Cited by 829 · see citing works

Information Conveyed by Vowels

Ladefoged, P., & Broadbent, D. E. (1957) · The Journal of the Acoustical Society of America · doi:10.1121/1.1908694 · Cited by 763 · see citing works

Finding consensus in speech recognition: word error minimization and other applications of confusion networks

Mangu, L., Brill, E., & Stolcke, A. (2000) · Computer Speech & Language · doi:10.1006/csla.2000.0152 · Cited by 646 · see citing works

Describing the emotional states that are expressed in speech

Cowie, R., & Cornelius, R. R. (2003) · Speech Communication · doi:10.1016/s0167-6393(02)00071-7 · Cited by 602 · see citing works

Semi-tied covariance matrices for hidden Markov models

Gales, M. (1999) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.759034 · Cited by 537 · see citing works

A database for speaker-independent digit recognition

Leonard, R. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1984.1172716 · Cited by 536 · see citing works

Phone-level pronunciation scoring and assessment for interactive language learning

Witt, S. M., & Young, S. J. (2000) · Speech Communication · doi:10.1016/s0167-6393(99)00044-8 · Cited by 535 · see citing works

Speech recognition by machines and humans

Lippmann, R. P. (1997) · Speech Communication · doi:10.1016/s0167-6393(97)00021-6 · Cited by 531 · see citing works

Eigenvoice modeling with sparse training data

Kenny, P. J., Boulianne, G., & Dumouchel, P. (2005) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2004.840940 · Cited by 475 · see citing works

Interaction between the native and second language phonetic subsystems

Flege, J. E., Schirru, C., & MacKay, I. R. A. (2003) · Speech Communication · doi:10.1016/s0167-6393(02)00128-0 · Cited by 474 · see citing works

Introduction to statistical pattern recognition

Davis, J. C. (1996) · Computers & Geosciences · doi:10.1016/0098-3004(96)00017-9 · Cited by 463 · see citing works

Effects of disfluencies, predictability, and utterance position on word form variation in English conversation

Bell, A., Jurafsky, D. S., Fosler‐Lussier, E., Girand, C., Gregory, M., & Gildea, D. (2003) · The Journal of the Acoustical Society of America · doi:10.1121/1.1534836 · Cited by 460 · see citing works

Speech communication : human and machine

O’Shaughnessy, D. D. (1987) · Medical Entomology and Zoology · Cited by 442 · see citing works

Speech Production and Speech Modelling

(1990) · Springer eBooks · doi:10.1007/978-94-009-2037-8 · Cited by 432 · see citing works

Speech communication, human and machine

Karjalainen, M. (1988) · Signal Processing · doi:10.1016/0165-1684(88)90074-6 · Cited by 412 · see citing works

A pattern recognition approach to voiced-unvoiced-silence classification with applications to speech recognition

Atal, B. S., & Rabiner, L. R. (1976) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1976.1162800 · Cited by 411 · see citing works

Prosody-based automatic detection of annoyance and frustration in human-computer dialog

Ang, J., Dhillon, R., Krupski, A., Shriberg, E. E., & Stolcke, A. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2002-559 · Cited by 390 · see citing works

Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition

Kumar, N., & Andreou, A. G. (1998) · Speech Communication · doi:10.1016/s0167-6393(98)00061-2 · Cited by 342 · see citing works

The phonetic bases of speaker recognition

Nolan, F. (1987) · Speech Communication · doi:10.1016/0167-6393(87)90039-2 · Cited by 336 · see citing works

Linear discriminant analysis for improved large vocabulary continuous speech recognition

Haeb‐Umbach, R., & Ney, H. (1992) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1992.225984 · Cited by 322 · see citing works

Multi-style training for robust isolated-word speech recognition

Lippmann, R. P., Martin, E., & Paul, D. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1987.1169544 · Cited by 313 · see citing works

Linear dimensionality reduction via a heteroscedastic extension of LDA: the Chernoff criterion

Duin, R. P. W., & Loog, M. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.13 · Cited by 293 · see citing works

Modeling of the glottal flow derivative waveform with application to speaker identification

Plumpe, M., Quatieri, T. F., & Reynolds, D. A. (1999) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.784109 · Cited by 289 · see citing works

Robust speech recognition using the modulation spectrogram

Kingsbury, B. E. D., Morgan, N. H., & Greenberg, S. (1998) · Speech Communication · doi:10.1016/s0167-6393(98)00032-6 · Cited by 273 · see citing works

Maximum likelihood modeling with Gaussian distributions for classification

Gopinath, R. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1998.675351 · Cited by 267 · see citing works

Speaker normalization using efficient frequency warping procedures

Lee, L., & Rose, R. C. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1996.541105 · Cited by 266 · see citing works

A study on speaker adaptation of the parameters of continuous density hidden Markov models

Lee, C.-H., Lin, C.-Y., & Juang, B.-H. (1991) · IEEE Transactions on Signal Processing · doi:10.1109/78.80902 · Cited by 263 · see citing works

Perceptual and Physical Space of Vowel Sounds

Pols, L. C. W., van der Kamp, L. J. T., & Plomp, R. (1969) · The Journal of the Acoustical Society of America · doi:10.1121/1.1911711 · Cited by 263 · see citing works

A parametric approach to vocal tract length normalization

Eide, E., & Gish, H. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1996.541103 · Cited by 259 · see citing works

Speech production and speech modelling

Gabbouj, M. (1991) · Signal Processing · doi:10.1016/0165-1684(91)90075-t · Cited by 257 · see citing works

Large vocabulary continuous speech recognition using HTK

Woodland, P. C., Odell, J. J., Valtchev, V., & Young, S. J. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1994.389562 · Cited by 250 · see citing works

A new statistical approach for the automatic segmentation of continuous speech signals

Andre-Obrecht, R. (1988) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.1486 · Cited by 244 · see citing works

Analysis and compensation of speech under stress and noise for environmental robustness in speech recognition

Hansen, J. H. L. (1996) · Speech Communication · doi:10.1016/s0167-6393(96)00050-7 · Cited by 242 · see citing works

On the use of bandpass liftering in speech recognition

Juang, B. H., Rabiner, L. R., & Wilpon, J. (1987) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1987.1165237 · Cited by 239 · see citing works

Phonetic feature systems for vowels

Nearey, T. M. (1978) · Cited by 236 · see citing works

Automatic scoring of pronunciation quality

Neumeyer, L., Franco, H., Digalakis, V., & Weintraub, M. (2000) · Speech Communication · doi:10.1016/s0167-6393(99)00046-1 · Cited by 229 · see citing works

Modeling pronunciation variation for ASR: A survey of the literature

Strik, H., & Cucchiarini, C. (1999) · Speech Communication · doi:10.1016/s0167-6393(99)00038-2 · Cited by 222 · see citing works

Robust recognition of children's speech

Potamianos, A., & Narayanan, S. S. (2003) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2003.818026 · Cited by 203 · see citing works

Normalization of vowels by vocal-tract length and its application to vowel identification

Wakita, H. (1977) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1977.1162929 · Cited by 197 · see citing works

Maximum likelihood discriminant feature spaces

Saon, G., Padmanabhan, M., Gopinath, R., & Chen, S. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2000.859163 · Cited by 194 · see citing works

A study of speech recognition for children and the elderly

Wilpon, J. G., & Jacobsen, C. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1996.541104 · Cited by 190 · see citing works

Efficient coding of LPC parameters by temporal decomposition

Atal, B. S. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1983.1172248 · Cited by 190 · see citing works

Combining active and semi-supervised learning for spoken language understanding

Tür, G., Hakkani-Tür, D. Z., & Schapire, R. E. (2004) · Speech Communication · doi:10.1016/j.specom.2004.08.002 · Cited by 187 · see citing works

Effects of speaking rate and word frequency on pronunciations in convertional speech

Fosler‐Lussier, E., & Morgan, N. H. (1999) · Speech Communication · doi:10.1016/s0167-6393(99)00035-7 · Cited by 161 · see citing works

A family of distortion measures based upon projection operation for robust speech recognition

Mansour, D., & Juang, B.-H. (1989) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.46548 · Cited by 159 · see citing works

Language accent classification in American English

Arslan, L. M., & Hansen, J. H. L. (1996) · Speech Communication · doi:10.1016/0167-6393(96)00024-6 · Cited by 157 · see citing works

Toward the Specification of Speech

Potter, R. K., & Steinberg, J. C. (1950) · The Journal of the Acoustical Society of America · doi:10.1121/1.1906694 · Cited by 152 · see citing works

On the automatic segmentation of speech signals

Svendsen, T. K., & Soong, F. K. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1987.1169628 · Cited by 146 · see citing works

Automatic text-independent pronunciation scoring of foreign language student speech

Neumeyer, L., Franco, H., Weintraub, M., & Price, P. J. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.1109/icslp.1996.607890 · Cited by 143 · see citing works

Speaker adaptation based on MAP estimation of HMM parameters

Lee, C.-H., & Gauvain, J.-L. (1993) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1993.319368 · Cited by 142 · see citing works

Automatic Recognition of Spontaneous Speech for Access to Multilingual Oral History Archives

Byrne, B., Doermann, D., Franz, M., Gustman, S., Hajič, J., Oard, D. W., Picheny, M., Psutka, J., Ramabhadran, B., Soergel, D., et al. (2004) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2004.828702 · Cited by 130 · see citing works

Combining multiple estimators of speaking rate

Morgan, N., & Fosler‐Lussier, E. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1998.675368 · Cited by 129 · see citing works

Combination of machine scores for automatic grading of pronunciation quality

Franco, H., Neumeyer, L., Digalakis, V., & Ronen, O. (2000) · Speech Communication · doi:10.1016/s0167-6393(99)00045-x · Cited by 128 · see citing works

TRAPS - classifiers of temporal patterns

Heřmanský, H., & Sharma, S. (1998) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.1998-524 · Cited by 125 · see citing works

Investigating syllabic structures and their variation in spontaneous French

Adda‐Decker, M., de Mareüil, P. B., Adda, G., & Lamel, L. F. (2005) · Speech Communication · doi:10.1016/j.specom.2005.03.006 · Cited by 125 · see citing works

Usefulness of phase spectrum in human speech perception

Paliwal, K. K., & Alsteris, L. D. (2003) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2003-611 · Cited by 125 · see citing works

Model-based approach to envelope and positive instantaneous frequency estimation of signals with speech applications

Kumaresan, R., & Rao, A. (1999) · The Journal of the Acoustical Society of America · doi:10.1121/1.426727 · Cited by 123 · see citing works

Maximum a posteriori linear regression for hidden Markov model adaptation

Chesta, C., Siohan, O., & Lee, C. (1999) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1999-56 · Cited by 122 · see citing works

Sub-band based recognition of noisy speech

Tibrewala, S., & Heřmanský, H. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1997.596173 · Cited by 122 · see citing works

Subband-based speech recognition

Bourlard, H., & Dupont, S. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1997.596172 · Cited by 114 · see citing works

Robust speaker-independent word recognition using static, dynamic and acceleration features: experiments with Lombard and noisy speech

Hanson, B. A., & Applebaum, T. H. (2002) · International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1990.115973 · Cited by 110 · see citing works

Cross-Domain and Cross-Language Portability of Acoustic Features Estimated by Multilayer Perceptrons

Stolcke, A., Grézl, F., Hwang, M.-Y., Lei, X., Morgan, N., & Vergyri, D. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2006.1660022 · Cited by 110 · see citing works

A comparison of several acoustic representations for speech recognition with degraded and undegraded speech

Hunt, M. J., & Lefebvre, C. (2003) · International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1989.266415 · Cited by 109 · see citing works

Automatic speech recognition for children

Potamianos, A., Narayanan, S. S., & Lee, S. (1997) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1997-623 · Cited by 108 · see citing works

Confidence measures for spontaneous speech recognition

Schaaf, T. K., & Kemp, T. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1997.596075 · Cited by 106 · see citing works

Speaker normalization based on frequency warping

Zhan, P., & Westphal, M. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1997.596118 · Cited by 103 · see citing works

Improving word accuracy with Gabor feature extraction

Kleinschmidt, M., & Gelbart, D. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2002-5 · Cited by 102 · see citing works

On using MLP features in LVCSR

Zhu, Q., Chen, B. Y., Morgan, N. H., & Stolcke, A. (2004) · Interspeech (USB) · doi:10.21437/interspeech.2004-337 · Cited by 98 · see citing works

The OGI kids² speech corpus and recognizers

Shobaki, K., Hosom, J.-P., & Cole, R. A. (2000) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2000-800 · Cited by 97 · see citing works

Children's speech recognition with application to interactive books and tutors

Hagen, A., Pellom, B. L., & Cole, R. A. (2004) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2003.1318426 · Cited by 96 · see citing works

Different aspects of expert pronunciation quality ratings and their relation to scores produced by speech recognition algorithms

Cucchiarini, C., Strik, H., & Boves, L. (2000) · Speech Communication · doi:10.1016/s0167-6393(99)00040-0 · Cited by 96 · see citing works

HMM clustering for connected word recognition

Rabiner, L. R., Lee, C.-H., Juang, B.-H., & Wilpon, J. G. (2003) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1989.266451 · Cited by 94 · see citing works

Product of power spectrum and group delay function for speech recognition

Zhu, D., & Paliwal, K. K. (2004) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2004.1325938 · Cited by 93 · see citing works

Combining articulatory and acoustic information for speech recognition in noisy and reverberant environments

Kirchhoff, K. (1998) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.1998-313 · Cited by 90 · see citing works

On the effects of speech rate in large vocabulary speech recognition systems

Siegler, M. A., & Stern, R. M. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1995.479672 · Cited by 90 · see citing works

Prosodic and other cues to speech recognition failures

Hirschberg, J., Litman, D. J., & Swerts, M. G. J. (2004) · Speech Communication · doi:10.1016/j.specom.2004.01.006 · Cited by 89 · see citing works

Speech recognition using on-line estimation of speaking rate

Morgan, N. H., Fosler, E., & Mirghafori, N. (1997) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1997-550 · Cited by 88 · see citing works

Investigating recognition of children's speech

Giuliani, D., & Gerosa, M. (2003) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2003.1202313 · Cited by 87 · see citing works

Robust prosodic features for speaker identification

Carey, M. J., Parris, E. S., Lloyd-Thomas, H., & Bennett, S. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.1109/icslp.1996.607979 · Cited by 86 · see citing works

Effects of phase on the perception of intervocalic stop consonants

Liu, L., He, J., & Palm, G. (1997) · Speech Communication · doi:10.1016/s0167-6393(97)00054-x · Cited by 86 · see citing works

Human and machine consonant recognition

Sroka, J. J., & Braida, L. D. (2005) · Speech Communication · doi:10.1016/j.specom.2004.11.009 · Cited by 86 · see citing works

Vocal Tract Length Normalization for Large Vocabulary Continuous Speech Recognition

Zhan, P., & Waibel, A. (1997) · doi:10.21236/ada333514 · Cited by 85 · see citing works

Frequency-domain linear prediction for temporal features

Athineos, M., & Ellis, D. P. W. (2004) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2003.1318451 · Cited by 85 · see citing works

Word perception in fast speech: artificially time-compressed vs. naturally produced fast speech

Janse, E. (2003) · Speech Communication · doi:10.1016/j.specom.2003.07.001 · Cited by 84 · see citing works

Fast accent identification and accented speech recognition

Kat, L. W., & Fung, P. (1999) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1999.758102 · Cited by 84 · see citing works

Long-term feature averaging for speaker recognition

Markel, J. D., Oshika, B. T., & Gray, A. (1977) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1977.1162961 · Cited by 82 · see citing works

On speaker-independent, speaker-dependent, and speaker-adaptive speech recognition

Huang, X., & Lee, K. F. (1993) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.222875 · Cited by 81 · see citing works

Tandem acoustic modeling in large-vocabulary recognition

Ellis, D. P. W., Singh, R., & Sivadas, S. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2001.940881 · Cited by 80 · see citing works

New methods in continuous Mandarin speech recognition

Chen, C. J., Gopinath, R. A., Monkowski, M. D., Picheny, M., & Shen, K. (1997) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1997-444 · Cited by 79 · see citing works

Hidden Markov model for Mandarin lexical tone recognition

Yang, W.-J., Lee, J.-C., Chang, Y.-C., & Wang, H.-C. (1988) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.1620 · Cited by 79 · see citing works

Speaker adaptive modeling by vocal tract normalization

Welling, L., Ney, H., & Kanthak, S. (2002) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2002.803435 · Cited by 79 · see citing works

What kind of pronunciation variation is hard for triphones to model?

Jurafsky, D. S., Ward, W., Banping, Z., Herold, K. C., Yu, X., & Sen, Z. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2001.940897 · Cited by 78 · see citing works

Analysis of speaker variability

Huang, C., Chen, T., Li, S. Z., Chang, E., & Zhou, J.-L. (2001) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2001-356 · Cited by 77 · see citing works

Acoustic Feature Combination for Robust Speech Recognition

Zolnay, A. G., Schlueter, R., & Ney, H. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2005.1415149 · Cited by 73 · see citing works

Generating non-native pronunciation variants for lexicon adaptation

Goronzy, S., Rapp, S., & Kompe, R. (2003) · Speech Communication · doi:10.1016/j.specom.2003.09.003 · Cited by 72 · see citing works

Distinctive features for use in an automatic speech recognition system

Eide, E. (2001) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2001-195 · Cited by 70 · see citing works

Using formant frequencies in speech recognition

Holmes, J. N., Holmes, W. J., & Garner, P. N. (1997) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1997-551 · Cited by 70 · see citing works

Error-responsive feedback mechanisms for speech recognizers

Chase, L. (1997) · Cited by 70 · see citing works

Oldenburg logatome speech corpus (OLLO) for speech recognition experiments with humans and machines

Wesker, T., Meyer, B. T., Wagener, K. C., Anemüller, J., Mertins, A., & Kollmeier, B. (2005) · Interspeech (USB) · doi:10.21437/interspeech.2005-485 · Cited by 68 · see citing works

Accent identification

Teixeira, C., Trancoso, I. M., & Serralheiro, A. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.1109/icslp.1996.607975 · Cited by 68 · see citing works

Coherent Envelope Detection for Modulation Filtering of Speech

Schimmel, S. M., & Atlas, L. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2005.1415090 · Cited by 65 · see citing works

Analysis of children's speech: duration, pitch and formants

Lee, S., Potamianos, A., & Narayanan, S. S. (1997) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1997-161 · Cited by 64 · see citing works

Improvements in children's speech recognition performance

Das, S. K., Nix, D. A., & Picheny, M. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1998.674460 · Cited by 64 · see citing works

Using accent-specific pronunciation modelling for robust speech recognition

Humphries, J. J., Woodland, P. C., & Pearce, D. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.1109/icslp.1996.607273 · Cited by 63 · see citing works

Language independent and language adaptive large vocabulary speech recognition

Schultz, T., & Waibel, A. (1998) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.1998-751 · Cited by 62 · see citing works

Pronunciation variants across system configuration, language and speaking style

Adda‐Decker, M., & Lamel, L. F. (1999) · Speech Communication · doi:10.1016/s0167-6393(99)00032-1 · Cited by 62 · see citing works

Pitch tracking and tone features for Mandarin speech recognition

Huang, H., & Seide, F. T. B. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2000.861942 · Cited by 60 · see citing works

Lexical modeling of non-native speech for automatic speech recognition

Livescu, K., & Glass, J. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2000.862074 · Cited by 59 · see citing works

Multiple-regression hidden Markov model

Fujinaga, K., Nakai, M., Shimodaira, H., & Sagayama, S. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2001.940880 · Cited by 58 · see citing works

A study of multilingual speech recognition

Weng, F., Bratt, H., Neumeyer, L., & Stolcke, A. (1997) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1997-138 · Cited by 58 · see citing works

Recognizing speech of goats, wolves, sheep and … non-natives

Compernolle, D. V. (2001) · Speech Communication · doi:10.1016/s0167-6393(00)00096-0 · Cited by 58 · see citing works

Scale transform in speech analysis

Umesh, S., Cohen, L. W., Marinovic, N., & Nelson, D. J. (1999) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.736329 · Cited by 58 · see citing works

Pronunciation modeling using a finite-state transducer representation

Hazen, T. J., Hetherington, I. L., Shu, H., & Livescu, K. (2005) · Speech Communication · doi:10.1016/j.specom.2005.03.004 · Cited by 57 · see citing works

Knowledge of language origin improves pronunciation accuracy of proper names

Llitjós, A. F., & Black, A. W. (2001) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2001-453 · Cited by 56 · see citing works

Mel-cepstrum modulation spectrum (MCMS) features for robust ASR

Tyagi, V., McCowan, I. A., Misra, H., & Bourlard, H. (2004) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2003.1318474 · Cited by 55 · see citing works

A speaker-stress resistant HMM isolated word recognizer

Paul, D. B. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1987.1169551 · Cited by 53 · see citing works

Cluster adaptive training for speech recognition

Gales, M. (1998) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.1998-743 · Cited by 52 · see citing works

Zeros of Z-transform representation with application to source-filter separation in speech

Bozkurt, B., Doval, B., d’Alessandro, C. R., & Dutoit, T. (2005) · IEEE Signal Processing Letters · doi:10.1109/lsp.2005.843770 · Cited by 52 · see citing works

Segmental Minimum Bayes-Risk Decoding for Automatic Speech Recognition

Goel, V., Kumar, S., & Byrne, B. (2004) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2004.825678 · Cited by 51 · see citing works

Language Model and Speaking Rate Adaptation for Spontaneous Presentation Speech Recognition

Nanjo, H., & Kawahara, T. (2004) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2004.828641 · Cited by 50 · see citing works

Automatic accent classification of foreign accented Australian English speech

Kumpf, K., & King, R. W. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.1109/icslp.1996.607964 · Cited by 47 · see citing works

Implicit modelling of pronunciation variation in automatic speech recognition

Hain, T. (2005) · Speech Communication · doi:10.1016/j.specom.2005.03.008 · Cited by 47 · see citing works

Theory and practice of acoustic confusability

Printz, H., & Olsen, P. A. (2002) · Computer Speech & Language · doi:10.1006/csla.2001.0188 · Cited by 47 · see citing works

Towards robustness to fast speech in ASR

Mirghafori, N., Fosler, E., & Morgan, N. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1996.541100 · Cited by 47 · see citing works

Understanding and improving speech recognition performance through the use of diagnostic tools

Eide, E. M., Gish, H., Jeanrenaud, P., & Mielke, A. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1995.479404 · Cited by 46 · see citing works

Using accent-specific pronunciation modelling for improved large vocabulary continuous speech recognition

Humphries, J. J., & Woodland, P. C. (1997) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1997-622 · Cited by 45 · see citing works

Detection of foreign speakers' pronunciation errors for second language training-preliminary results

Eskénazi, M. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.1109/icslp.1996.607892 · Cited by 45 · see citing works

Comparison and combination of features in a hybrid HMM/MLP and a HMM/GMM speech recognition system

Pujol, P., Pol, S., Nadeu, C., Hagen, A., & Bourlard, H. A. (2004) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2004.834466 · Cited by 44 · see citing works

LINGUISTIC DISSECTION OF SWITCHBOARD-CORPUS AUTOMATIC SPEECH RECOGNITION SYSTEMS

Greenberg, S., & Chang, S. (2000) · Cited by 44 · see citing works

Comparative experiments on large vocabulary speech recognition

Kubala, F., Anastasakos, A., Makhoul, J., Nguyen, L. H. B., Schwartz, R. M., & Zavaliagkos, E. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1994.389232 · Cited by 44 · see citing works

Towards improving ASR robustness for PSN and GSM telephone applications

Mokbel, C., Mauuary, L., Karray, L., Jouvet, D., Monné, J., Simonin, J., & Bartkova, K. (1997) · Speech Communication · doi:10.1016/s0167-6393(97)00042-3 · Cited by 44 · see citing works

Fast speakers in large vocabulary continuous speech recognition: analysis & antidotes.

Mirghafori, N., Fosler, E., & Morgan, N. H. (1995) · Cited by 43 · see citing works

Robust speech recognition in Noisy Environments: The 2001 IBM spine evaluation system

Kingsbury, B. E. D., Saon, G., Mangu, L., Padmanabhan, M., & Sarikaya, R. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.5743652 · Cited by 43 · see citing works

On the robust incorporation of formant features into hidden Markov models for automatic speech recognition

Garner, P. N., & Holmes, W. J. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1998.674352 · Cited by 43 · see citing works

Speech Recognition With Auxiliary Information

Stephenson, T. A., Magimai.-Doss, M., & Bourlard, H. (2004) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2003.822631 · Cited by 43 · see citing works

The use of cepstral means in conversational speech recognition

Westphal, M. (1997) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1997-120 · Cited by 42 · see citing works

Inclusion of temporal information into features for speech recognition

Milner, B. P. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.1109/icslp.1996.607093 · Cited by 40 · see citing works

Trapping conversational speech: extending TRAP/tandem approaches to conversational telephone speech recognition

Morgan, N., Chen, B. Y., Zhu, Q., & Stolcke, A. (2004) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2004.1326041 · Cited by 40 · see citing works

Robust speech recognition using a voiced-unvoiced feature

Zolnay, A. G., Schlüter, R., & Ney, H. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2002-38 · Cited by 39 · see citing works

Automatic speech recognition using dynamic bayesian networks with both acoustic and articulatory variables

Stephenson, T. A., Bourlard, H. A., Samy Bengio, A., & Morris, A. (2000) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2000-428 · Cited by 38 · see citing works

Speaker Identification and Message Identification in Speech Recognition

Garvin, P. L., & Ladefoged, P. (1963) · Phonetica · doi:10.1159/000258404 · Cited by 38 · see citing works

Modelling asynchrony in speech using elementary single-signal decomposition

Tomlinson, M. J., Russell, M. J., Moore, R. K., Buckland, A. P., & Fawley, M. A. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1997.596171 · Cited by 38 · see citing works

Frequency-related representation of speech

Paliwal, K. K., & Atal, B. S. (2003) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2003-17 · Cited by 38 · see citing works

Use of periodicity and jitter as speech recognition features

Thomson, D. L., & Chengalvarayan, R. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1998.674357 · Cited by 38 · see citing works

Multilingual speech recognition in seven languages

Uebler, U. (2001) · Speech Communication · doi:10.1016/s0167-6393(00)00095-9 · Cited by 37 · see citing works

Discriminative linear transforms for feature normalization and speaker adaptation in HMM estimation

Tsakalidis, S., Doumpiotis, V., & Byrne, B. (2005) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2005.845806 · Cited by 37 · see citing works

Recognize tone languages using pitch information on the main vowel of each syllable

Chen, C. J., Li, H., Shen, L., & Fu, G. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2001.940767 · Cited by 36 · see citing works

Maximum a posteriori adaptation for large scale HMM recognizers

Zavaliagkos, G., Schwartz, R. M., & McDonough, J. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1996.543223 · Cited by 36 · see citing works

Innovative approaches for large vocabulary name recognition

Gao, Y., Ramabhadran, B., Chen, J., Erdoğan, H., & Picheny, M. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2001.940765 · Cited by 34 · see citing works

KIDS: A database of children’s speech

Eskénazi, M. (1996) · The Journal of the Acoustical Society of America · doi:10.1121/1.416340 · Cited by 34 · see citing works

An improved sub-word based speech recognizer

Svendsen, T. K., Paliwal, K. K., Harborg, E., & Husøy, P. O. (2003) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1989.266375 · Cited by 34 · see citing works

GDNN: a gender-dependent neural network for continuous speech recognition

Konig, Y., & Morgan, N. (2003) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1992.226966 · Cited by 33 · see citing works

Spectral Signal Processing for ASR

Hunt, M. J. (1999) · Cited by 33 · see citing works

Comparing speech recognition for adults and children

Elenius, D., & Blomberg, M. (2004) · Cited by 31 · see citing works

Auditory-based acoustic distinctive features and spectral cues for automatic speech recognition using a multi-stream paradigm

Tolba, H., Selouani, S., & O’Shaughnessy, D. D. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.5743869 · Cited by 31 · see citing works

Evaluation of formant-like features on an automatic vowel classification task

de Wet, F., Weber, K., Boves, L., Cranen, B., Samy Bengio, A., & Bourlard, H. A. (2004) · The Journal of the Acoustical Society of America · doi:10.1121/1.1781620 · Cited by 30 · see citing works

Speaking-rate dependent decoding and adaptation for spontaneous lecture speech recognition

Nanjo, H., & Kawahara, T. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.5743820 · Cited by 30 · see citing works

Towards speech rate independence in large vocabulary continuous speech recognition

Martinez, F., Tapias, D., & Álvarez, J. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1998.675367 · Cited by 30 · see citing works

Continuous speech recognition using joint features derived from the modified group delay function and MFCC

Hegde, R. M., Murthy, H. A., & Gadde, V. R. R. (2004) · Interspeech (USB) · doi:10.21437/interspeech.2004-333 · Cited by 29 · see citing works

Robust ASR front-end using spectral-based and discriminant features: experiments on the Aurora tasks

Benı́tez, C., Burget, L., Chen, B. Y., Dupont, S., Garudadri, H., Hynek Hermansky, B., Jain, P., Kajarekar, S. S., Morgan, N. H., & Sivadas, S. (2001) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2001-115 · Cited by 28 · see citing works

Multilingual speech recognition for flexible vocabularies

Bonaventura, P., Gallocchio, F., & Micca, G. (1997) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1997-137 · Cited by 28 · see citing works

An efficient repair procedure for quick transcriptions

Venkataraman, A., Stolcke, A., Wang, W., Vergyri, D., Zheng, J., & Gadde, V. R. R. (2004) · Interspeech (USB) · doi:10.21437/interspeech.2004-193 · Cited by 28 · see citing works

On The Use Of Phase Information In Speech Recognition

Bozkurt, B., & Couvreur, L. (2005) · doi:10.5281/zenodo.39041 · Cited by 28 · see citing works

Extensions to phone-state decision-tree clustering: single tree and tagged clustering

Paul, D. B. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1997.596231 · Cited by 28 · see citing works

Speaker clustering and transformation for speaker adaptation in large-vocabulary speech recognition systems

Padmanabhan, M., Bahl, L. R., Nahamoo, D., & Picheny, M. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1996.543217 · Cited by 28 · see citing works

The BBN BYBLOS Continuous Speech Recognition system

Schwartz, R. M., Barry, C., Chow, Y.-L., Derr, A., Feng, M.-W., Kimball, O., Kubala, F., Makhoul, J. I., & Vandegrift, J. (1989) · DARPA Workshop on Speech and Natural Language (HLT) · doi:10.3115/100964.100968 · Cited by 28 · see citing works

Pitch dependent phone modelling for HMM based speech recognition

Singer, H., & Sagayama, S. (1992) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1992.225918 · Cited by 28 · see citing works

Dynamic HMM selection for continuous speech recognition

Hain, T., & Woodland, P. C. (1999) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1999-339x · Cited by 27 · see citing works

Speaker normalization and adaptation using second-order connectionist networks

Watrous, R. L. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.182692 · Cited by 27 · see citing works

Multiple-cluster adaptive training schemes

Gales, M. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2001.940842 · Cited by 26 · see citing works

Flat-spectrum speech

Schroeder, M. R., & Strube, H. W. (1986) · The Journal of the Acoustical Society of America · doi:10.1121/1.393292 · Cited by 26 · see citing works

A framework for predicting speech recognition errors

Fosler‐Lussier, E., Amdal, I., & Kuo, H.-K. J. (2005) · Speech Communication · doi:10.1016/j.specom.2005.03.003 · Cited by 26 · see citing works

Generalized mixture of HMMs for continuous speech recognition

Korkmazskiy, F., Juang, B.-H. F., & Soong, F. K. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1997.596220 · Cited by 26 · see citing works

Adaptation algorithms for large scale HMM recognizers

Zavaliagkos, G., Schwartz, R., McDonough, J. E., & Makhoul, J. I. (1995) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1995-283 · Cited by 25 · see citing works

Speaker modification with LPC pole analysis

Slifka, J., & Anderson, T. R. (2002) · International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1995.479680 · Cited by 25 · see citing works

On the limits of speech recognition in noise

Peters, S. D., Stubley, P., & Valin, J.-M. (1999) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1999.758138 · Cited by 25 · see citing works

Off-line acoustic modelling of non-native accents

Witt, S. M., & Young, S. (1999) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1999-348x · Cited by 24 · see citing works

Recognition of read and spontaneous children's speech using two new corpora

Russell, M. J., D’Arcy, S., & Wong, L. P. (2004) · Interspeech (USB) · doi:10.21437/interspeech.2004-560 · Cited by 24 · see citing works

Spontaneous speech recognition using a massively parallel decoder

Shinozaki, T., & Furui, S. (2004) · Interspeech (USB) · doi:10.21437/interspeech.2004-185 · Cited by 24 · see citing works

Algorithmic bias in education

Baker, R. S., & Hawn, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00285-9 · Cited by 993 · see citing works

Mel Frequency Cepstral Coefficient and its Applications: A Review

Abdul, Z. K., & Al‐Talabani, A. K. (2022) · IEEE Access · doi:10.1109/access.2022.3223444 · Cited by 537 · see citing works

Cobot programming for collaborative industrial tasks: An overview

Zaatari, S. E., Marei, M., Li, W., & Usman, Z. (2019) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2019.03.003 · Cited by 470 · see citing works

Audio-driven facial animation by joint end-to-end learning of pose and emotion

Karras, T., Aila, T., Laine, S., Herva, A., & Lehtinen, J. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073658 · Cited by 435 · see citing works

Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing

Jung, Y.-H., Hong, S. K., Wang, H. S., Han, J. H., Pham, T. X., Park, H., Kim, J., Kang, S., Yoo, C. D., & Lee, K. J. (2019) · Advanced Materials · doi:10.1002/adma.201904020 · Cited by 339 · see citing works

Neural mechanisms for voice recognition

Andics, A., McQueen, J. M., Petersson, K. M., Gál, V., Rudas, G., & Vidnyánszky, Z. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.048 · Cited by 222 · see citing works

Invited paper: Automatic speech recognition: History, methods and challenges

O’Shaughnessy, D. D. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.05.008 · Cited by 198 · see citing works

Conversational Agents: Goals, Technologies, Vision and Challenges

Allouch, M., Azaria, A., & Azoulay, R. (2021) · Sensors · doi:10.3390/s21248448 · Cited by 168 · see citing works

Towards a neurocomputational model of speech production and perception

Kröger, B. J., Kannampuzha, J., & Neuschaefer‐Rube, C. (2008) · Speech Communication · doi:10.1016/j.specom.2008.08.002 · Cited by 158 · see citing works

Difficulties in Automatic Speech Recognition of Dysarthric Speakers and Implications for Speech-Based Applications Used by the Elderly: A Literature Review

Young, V., & Mihailidis, A. (2010) · Assistive Technology · doi:10.1080/10400435.2010.483646 · Cited by 149 · see citing works

Spectro-temporal modulation subspace-spanning filter bank features for robust automatic speech recognition

Schädler, M. R., Meyer, B. T., & Kollmeier, B. (2012) · The Journal of the Acoustical Society of America · doi:10.1121/1.3699200 · Cited by 129 · see citing works

MuST-C: A multilingual corpus for end-to-end speech translation

Cattoni, R., Gangi, M. A. D., Bentivogli, L., Negri, M., & Turchi, M. (2021) · Computer Speech and Language · doi:10.1016/j.csl.2020.101155 · Cited by 192 · see citing works

Brain-Computer Interface: Applications to Speech Decoding and Synthesis to Augment Communication

Luo, S., Rabbani, Q., & Crone, N. E. (2022) · Neurotherapeutics · doi:10.1007/s13311-022-01190-2 · Cited by 106 · see citing works

Neural dynamics of phoneme sequences reveal position-invariant code for content and order

Gwilliams, L., King, J.-R., Marantz, A., & Poeppel, D. (2022) · Nature Communications · doi:10.1038/s41467-022-34326-1 · Cited by 108 · see citing works

A neural speech decoding framework leveraging deep learning and speech synthesis

Chen, X., Wang, R., Khalilian-Gourtani, A., Yu, L., Dugan, P. C., Friedman, D. J., Doyle, W., Devinsky, O., Wang, Y., & Flinker, A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00824-8 · Cited by 102 · see citing works

A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders

Voleti, R., Liss, J. M., & Berisha, V. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2952087 · Cited by 96 · see citing works

Nonlinear time-warping made simple: A step-by-step tutorial on underwater acoustic modal separation with a single hydrophone

Bonnel, J., Thode, A. M., Wright, D., & Chapman, R. (2020) · The Journal of the Acoustical Society of America · doi:10.1121/10.0000937 · Cited by 94 · see citing works

Improving Transformer-based Speech Recognition Using Unsupervised Pre-trainingPreprint

Jiang, D., Lei, X., Li, W., Luo, N., Hu, Y., Zou, W., & Li, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.09932 · Cited by 94 · see citing works

Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

Moses, D. A., Mesgarani, N., Leonard, M. K., & Chang, E. F. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/5/056004 · Cited by 92 · see citing works

Conversational Information Seeking

Zamani, H., Trippas, J. R., Dalton, J., & Radlinski, F. (2023) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000081 · Cited by 124 · see citing works

Cutting-edge communication and learning assistive technologies for disabled children: An artificial intelligence perspective

Zdravkova, K., Krasniqi, V., Dalipi, F., & Ferati, M. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.970430 · Cited by 84 · see citing works

Improving Real-Time Captioning Experiences for Deaf and Hard of Hearing Students

Kawas, S., Karalis, G., Wen, T., & Ladner, R. E. (2016) · Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/2982142.2982164 · Cited by 82 · see citing works

Pattern recognition with magnonic holographic memory device

Kozhevnikov, A., Gertz, F., Dudko, G. M., Filimonov, Y., & Khitun, A. (2015) · Applied Physics Letters · doi:10.1063/1.4917507 · Cited by 80 · see citing works

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

Mustafa, M. K., Allen, T. J., & Appiah, K. (2017) · Neural Computing and Applications · doi:10.1007/s00521-017-3028-2 · Cited by 80 · see citing works

A Phonemic-Based Tactile Display for Speech Communication

Reed, C. M., Tan, H. Z., Perez, Z. D., Wilson, E., Severgnini, F. M., Jung, J., Martinez, J. S., Jiao, Y., Israr, A., Lau, F. W. Y., et al. (2018) · IEEE Transactions on Haptics · doi:10.1109/toh.2018.2861010 · Cited by 79 · see citing works

Speech Emotion Recognition Using Quaternion Convolutional Neural Networks

Muppidi, A., & Radfar, M. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414248 · Cited by 72 · see citing works

A Study on Invariance of $f$-Divergence and Its Application to Speech Recognition

Qiao, Y., & Minematsu, N. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2010.2047340 · Cited by 72 · see citing works

Evaluating automatic speech recognition-based language learning systems: a case study

van Doremalen, J., Boves, L., Jozef Colpaert, B., Catia Cucchiarini, A., & Strik, H. (2016) · Computer Assisted Language Learning · doi:10.1080/09588221.2016.1167090 · Cited by 72 · see citing works

Speech emotion recognition research: an analysis of research focus

Mustafa, M. B., Yusoof, M., Don, Z. M., & Malekzadeh, M. (2018) · International Journal of Speech Technology · doi:10.1007/s10772-018-9493-x · Cited by 72 · see citing works

Effects of non-driving-related-task modality and road geometry on eye movements, lane-keeping performance, and workload while driving

Jeong, H., & Liu, Y. (2018) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2018.10.015 · Cited by 66 · see citing works

Spoken Instruction Understanding in Air Traffic Control: Challenge, Technique, and Application

Lin, Y. (2021) · Aerospace · doi:10.3390/aerospace8030065 · Cited by 66 · see citing works

On the design of automatic voice condition analysis systems. Part I: Review of concepts and an insight to the state of the art

Gómez-García, J. A., Moro-Velázquez, L., & Godino-Llorente, J. I. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.12.024 · Cited by 61 · see citing works

On Cross-Corpus Generalization of Deep Learning Based Speech Enhancement

Pandey, A., & Wang, D. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3016487 · Cited by 58 · see citing works

Application of big data and artificial intelligence in epidemic surveillance and containment

Jiao, Z., Ji, H., Yan, J., & Qi, X. (2022) · Intelligent Medicine · doi:10.1016/j.imed.2022.10.003 · Cited by 61 · see citing works

Robust Speech Recognition Using Generative Adversarial Networks

Sriram, A., Jun, H., Gaur, Y., & Satheesh, S. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462456 · Cited by 53 · see citing works

Characterizing Dysarthria Diversity for Automatic Speech Recognition: A Tutorial From the Clinical Perspective

Rowe, H. P., Gutz, S. E., Maffei, M. F., Tomanek, K., & Green, J. R. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.770210 · Cited by 52 · see citing works

An integrated system for voice command recognition and emergency detection based on audio signals

Principi, E., Squartini, S., Bonfigli, R., Ferroni, G., & Piazza, F. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.02.036 · Cited by 52 · see citing works

Sound event recognition in unstructured environments using spectrogram image processing

Dennis, J. W. (2014) · doi:10.32657/10356/59272 · Cited by 52 · see citing works

Trends and Directions in Computer-Assisted Pronunciation Training

Fouz-González, J. (2015) · Palgrave Macmillan UK eBooks · doi:10.1057/9781137509437_14 · Cited by 51 · see citing works

TASK-BASED PRONUNCIATION TEACHING AND RESEARCH

Mora, J. C., & Levkina, M. (2017) · Studies in Second Language Acquisition · doi:10.1017/s0272263117000183 · Cited by 54 · see citing works

Safety First: Conversational Agents for Health Care

Bickmore, T., Trinh, H., Asadi, R., & Olafsson, S. (2018) · Human–Computer Interaction Series · doi:10.1007/978-3-319-95579-7_3 · Cited by 50 · see citing works

Optimizing Automatic Speech Recognition for Low-Proficient Non-Native Speakers

van Doremalen, J., Catia Cucchiarini, A., & Strik, H. (2009) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1155/2010/973954 · Cited by 49 · see citing works

The quest for generalizations over consonants: Asymmetries between consonants and vowels are not the by-product of acoustic differences

Toro, J. M., Shukla, M., Nespor, M., & Endress, A. D. (2008) · Perception & Psychophysics · doi:10.3758/pp.70.8.1515 · Cited by 48 · see citing works

Silent Speech Recognition with Strain Sensors and Deep Learning Analysis of Directional Facial Muscle Movement

Yoo, H., Kim, E., Chung, J. W., Cho, H. K., Jeong, S., Kim, H., Jang, D., Kim, H., Yoon, J., Lee, G. H., et al. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c14918 · Cited by 45 · see citing works

The relationship between perceptual disturbances in dysarthric speech and automatic speech recognition performance

Tu, M., Wisler, A. A., Berisha, V., & Liss, J. (2016) · The Journal of the Acoustical Society of America · doi:10.1121/1.4967208 · Cited by 46 · see citing works

A critical review and analysis on techniques of speech recognition: The road ahead

Haridas, A. V., Marimuthu, R., & Sivakumar, V. G. (2018) · International Journal of Knowledge-based and Intelligent Engineering Systems · doi:10.3233/kes-180374 · Cited by 46 · see citing works

Effects of public vs. private automated transcripts on multiparty communication between native and non-native english speakers

Gao, G., Yamashita, N., Hautasaari, A., Echenique, A., & Fussell, S. R. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557303 · Cited by 45 · see citing works

Using DTW neural–based MFCC warping to improve emotional speech recognition

Sheikhan, M., Gharavian, D., & Ashoftedel, F. (2011) · Neural Computing and Applications · doi:10.1007/s00521-011-0620-8 · Cited by 45 · see citing works

Using Automatic Speech Recognition Technology with Elicited Oral Response Testing

Cox, T. L., & Davies, R. S. (2012) · CALICO Journal · doi:10.11139/cj.29.4.601-618 · Cited by 45 · see citing works

Optimization of data-driven filterbank for automatic speaker verification

Sarangi, S. K., Sahidullah, M., & Saha, G. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102795 · Cited by 45 · see citing works

Recognizing words across regional accents: the role of perceptual assimilation in lexical competition

Best, C. T., Shaw, J. A., & Clancy, E. M. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-504 · Cited by 44 · see citing works

A customized residual neural network and bi-directional gated recurrent unit-based automatic speech recognition model

Reza, S., Ferreira, M. C., Machado, J. J. M., & Tavares, J. M. R. S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119293 · Cited by 44 · see citing works

AI-based automated speech therapy tools for persons with speech sound disorder: a systematic literature review

Deka, C., Shrivastava, A., Abraham, A. K., Nautiyal, S., & Chauhan, P. (2025) · Speech, Language and Hearing · doi:10.1080/2050571x.2024.2359274 · Cited by 44 · see citing works

Investigation of broadcast-audio semantic analysis scenarios employing radio-programme-adaptive pattern classification

Kotsakis, R., Kalliris, G. M., & Dimoulas, C. A. (2012) · Speech Communication · doi:10.1016/j.specom.2012.01.004 · Cited by 43 · see citing works

Why we need to investigate casual speech to truly understand language production, processing and the mental lexicon

Tucker, B. V., & Ernestus, M. (2016) · The Mental Lexicon · doi:10.1075/ml.11.3.03tuc · Cited by 43 · see citing works

Performance evaluation of sequentially combined heterogeneous feature streams for Hindi speech recognition system

Aggarwal, R. K., & Dave, M. (2011) · Telecommunication Systems · doi:10.1007/s11235-011-9623-0 · Cited by 42 · see citing works

Automatic accent identification as an analytical tool for accent robust automatic speech recognition

Najafian, M., & Russell, M. J. (2020) · Speech Communication · doi:10.1016/j.specom.2020.05.003 · Cited by 42 · see citing works

The physiological microphone (PMIC): A competitive alternative for speaker assessment in stress detection and speaker verification

Patil, S. A., & Hansen, J. H. L. (2009) · Speech Communication · doi:10.1016/j.specom.2009.11.006 · Cited by 42 · see citing works

Analysis of Documentation Speed Using Web-Based Medical Speech Recognition Technology: Randomized Controlled Trial

Vogel, M., Kaisers, W., Waßmuth, R., & Mayatepek, E. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.5072 · Cited by 42 · see citing works

Non-Autoregressive ASR Modeling Using Pre-Trained Language Models for Chinese Speech Recognition

Yu, F.-H., Chen, K.-Y., & Lu, K.-H. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3166400 · Cited by 39 · see citing works

Automatic analysis of pronunciations for children with speech sound disorders

Dudy, S., Bedrick, S. D., Asgari, M., & Kain, A. B. (2017) · Computer Speech & Language · doi:10.1016/j.csl.2017.12.006 · Cited by 38 · see citing works

ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems

Lin, Y., Yang, B., Li, L., Guo, D., Zhang, J., Chen, H., & Zhang, Y. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107847 · Cited by 38 · see citing works

Computer Audition for Healthcare: Opportunities and Challenges

Qian, K., Li, X., Li, H., Li, S., Li, W., Ning, Z., Yu, S., Hou, L., Tang, G., Lu, J., et al. (2020) · Frontiers in Digital Health · doi:10.3389/fdgth.2020.00005 · Cited by 37 · see citing works

A study on the challenges and opportunities of speech recognition for Bengali language

Mridha, M. F., Ohi, A. Q., Hamid, M. A., & Monowar, M. M. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10083-3 · Cited by 37 · see citing works

Harnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences

Wilson, B. S., Tucci, D. L., Moses, D. A., Chang, E. F., Young, N. M., Zeng, F., Lesica, N. A., Bur, A. M., Kavookjian, H. L., Mussatto, C. C., et al. (2022) · Journal of the Association for Research in Otolaryngology · doi:10.1007/s10162-022-00846-2 · Cited by 36 · see citing works

Socio-technical Imaginaries: Envisioning and Understanding AI Parenting Supports through Design Fiction

Petsolari, M., Ibrahim, S. B., & Slovak, P. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642619 · Cited by 35 · see citing works

Effect of speech-intrinsic variations on human and automatic recognition of spoken phonemes

Meyer, B. T., Brand, T., & Kollmeier, B. (2011) · The Journal of the Acoustical Society of America · doi:10.1121/1.3514525 · Cited by 35 · see citing works

An Exploration of Captioning Practices and Challenges of Individual Content Creators on YouTube for People with Hearing Impairments

Li, F. M., Lu, C., Lu, Z., Carrington, P., & Truong, K. N. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512922 · Cited by 35 · see citing works

Human phoneme recognition depending on speech-intrinsic variability

Meyer, B. T., Jürgens, T. P., Wesker, T., Brand, T., & Kollmeier, B. (2010) · The Journal of the Acoustical Society of America · doi:10.1121/1.3493450 · Cited by 35 · see citing works

Applications of Zeroing Neural Networks: A Survey

Wang, T., Zhang, Z., Huang, Y., Liao, B., & Li, S. (2024) · IEEE Access · doi:10.1109/access.2024.3382189 · Cited by 33 · see citing works

Unsupervised model selection for recognition of regional accented speech

Najafian, M., DeMarco, A., Cox, S., & Russell, M. J. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-495 · Cited by 33 · see citing works

Validity of Off-the-Shelf Automatic Speech Recognition for Assessing Speech Intelligibility and Speech Severity in Speakers With Amyotrophic Lateral Sclerosis

Gutz, S. E., Stipancic, K. L., Yunusova, Y., Berry, J. D., & Green, J. R. (2022) · Journal of Speech Language and Hearing Research · doi:10.1044/2022_jslhr-21-00589 · Cited by 33 · see citing works

Multimodal Affect Recognition: Current Approaches and Challenges

Osman, H. A., & Falk, T. (2017) · InTech eBooks · doi:10.5772/65683 · Cited by 29 · see citing works

Exploration of Automatic Speech Recognition for Deaf and Hard of Hearing Students in Higher Education Classes

Butler, J., Trager, B., & Behm, B. (2019) · Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3308561.3353772 · Cited by 31 · see citing works

Sources of variability in consonant perception of normal-hearing listeners

Zaar, J., & Dau, T. (2015) · The Journal of the Acoustical Society of America · doi:10.1121/1.4928142 · Cited by 30 · see citing works

A Further Study of Unsupervised Pretraining for Transformer Based Speech Recognition

Jiang, D., Li, W., Zhang, R., Cao, M. Y., Luo, N., Yang, H., Zou, W., Han, K., & Li, X. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414539 · Cited by 30 · see citing works

Handwritten Text Recognition: A Survey

Garrido-Munoz, C., Ríos-Vila, A., & Calvo-Zaragoza, J. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3646002 · Cited by 29 · see citing works

Voice Pathology Detection Using Modulation Spectrum-Optimized Metrics

Moro-Velázquez, L., Gómez-García, J. A., & Godino-Llorente, J. I. (2016) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2016.00001 · Cited by 29 · see citing works

Neural dynamics of phoneme sequences: Position-invariant code for content and orderPreprint

Gwilliams, L., King, J.-R., Marantz, A., & Poeppel, D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.04.025684 · Cited by 29 · see citing works

Acoustic Analysis for Automatic Speech Recognition

O’Shaughnessy, D. D. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2013.2251592 · Cited by 28 · see citing works

Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions

Yang, L. W. Y., Ng, W. Y., Foo, L. L., Liu, Y., Yan, M., Lei, X., Zhang, X., & Ting, D. S. W. (2021) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000789 · Cited by 28 · see citing works

Contextual invariant-integration features for improved speaker-independent speech recognition

Müller, F., & Mertins, A. (2011) · Speech Communication · doi:10.1016/j.specom.2011.02.002 · Cited by 28 · see citing works

How should voice assistants be heard? The mitigating effect of verbal and vocal warmth in voice assistant service failure

Huang, B., & Sénécal, S. (2023) · The Service Industries Journal · doi:10.1080/02642069.2023.2208522 · Cited by 27 · see citing works

Adaptive Audio-Based Context Recognition

Dargie, W. (2009) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2009.2015676 · Cited by 27 · see citing works

Optimal Combination of Neural Temporal Envelope and Fine Structure Cues to Explain Speech Identification in Background Noise

Moon, I. J., Won, J.-H., Park, M., Ives, D. T., Nie, K., Heinz, M. G., Lorenzi, C., & Rubinstein, J. T. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1025-14.2014 · Cited by 27 · see citing works

Effect of aging on speech features and phoneme recognition: a study on Bengali voicing vowels

Das, B., Mandal, S., Mitra, P., & Basu, A. (2012) · International Journal of Speech Technology · doi:10.1007/s10772-012-9147-3 · Cited by 27 · see citing works

Automatic Recognition of Element Classes and Boundaries in the Birdsong with Variable Sequences

Koumura, T., & Okanoya, K. (2016) · PLoS ONE · doi:10.1371/journal.pone.0159188 · Cited by 26 · see citing works

Learning through intuitive interface: A case study on preschool learning

Lai, N. K., Ang, T. F., Por, L. Y., & Liew, C. S. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.08.015 · Cited by 26 · see citing works

Cross-word Arabic pronunciation variation modeling for speech recognition

AbuZeina, D., Al-Khatib, W. G., Elshafei, M., & Al-Muhtaseb, H. (2011) · International Journal of Speech Technology · doi:10.1007/s10772-011-9098-0 · Cited by 26 · see citing works

Adaptation to mis-pronounced speech: evidence for a prefrontal-cortex repair mechanism

Blanco-Elorrieta, E., Gwilliams, L., Marantz, A., & Liina Pylkkänen, A. (2021) · Scientific Reports · doi:10.1038/s41598-020-79640-0 · Cited by 25 · see citing works

Speech diversity and speech interfaces: considering an inclusive future through stammering

Clark, L., Cowan, B. R., Roper, A., Lindsay, S., & Sheers, O. (2020) · CIU · doi:10.1145/3405755.3406139 · Cited by 30 · see citing works

Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System

Hasija, T., Kadyan, V., Guleria, K., Alharbi, A., Alyami, H., & Goyal, N. (2022) · Sustainability · doi:10.3390/su14020614 · Cited by 25 · see citing works

Convex Weighting Criteria for Speaking Rate Estimation

Jiao, Y., Berisha, V., Tu, M., & Liss, J. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2434213 · Cited by 25 · see citing works

On Pole-Zero Model Estimation Methods Minimizing a Logarithmic Criterion for Speech Analysis

Marelli, D., & Balázs, P. (2009) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2009.2025544 · Cited by 25 · see citing works

Adversarial Learning of Raw Speech Features for Domain Invariant Speech Recognition

Tripathi, A., Mohan, A., Anand, S., & Singh, M. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462452 · Cited by 24 · see citing works

Radio2Text

Zhao, R., Yu, J., Zhao, H., & Ngai, E. C. ‐. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3610873 · Cited by 24 · see citing works

Development of Application Specific Continuous Speech Recognition System in Hindi

Gaurav, G., Deiv, D. S., Sharma, G. K., & Bhattacharya, M. (2012) · Journal of Signal and Information Processing · doi:10.4236/jsip.2012.33052 · Cited by 24 · see citing works

Speech Structure and Its Application to Robust Speech Processing

Minematsu, N., Asakawa, S., Suzuki, M., & Qiao, Y. (2010) · New Generation Computing · doi:10.1007/s00354-009-0091-y · Cited by 24 · see citing works

Exploring the Effect of Differences in the Acoustic Correlates of Adults' and Children's Speech in the Context of Automatic Speech Recognition

Ghai, S., & Sinha, R. (2010) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1155/2010/318785 · Cited by 23 · see citing works

Methods for eliciting, annotating, and analyzing databases for child speech development

Beckman, M. E., Plummer, A. R., Munson, B., & Reidy, P. F. (2017) · Computer Speech & Language · doi:10.1016/j.csl.2017.02.010 · Cited by 23 · see citing works

Impact of variabilities on speech recognition

Benzeguiba, M., Mori, R. D., Deroo, O., Dupont, S., Jouvet, D., Fissore, L., Laface, P., Mertins, A., Ris, C., Rose, R. C., et al. (2006) · PORTO@Iris · Cited by 17 · see citing works

Frequency spectrum deviation between speakers

Saito, S., & Itakura, F. (1983) · Speech Communication · doi:10.1016/0167-6393(83)90014-6 · Cited by 2 · see citing works

Automatic Speech Recognition and Intrinsic Speech Variation

Benzeguiba, M., Mori, R. D., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., Fissore, L., Laface, P., Mertins, A., Ris, C., et al. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2006.1661452 · Cited by 9 · see citing works

Automatic Speech Recognition: A Review

Katyal, A., Kaur, A., & Gill, J. (2015)

Challenges and Issues in Adopting Speech Recognition

Sahu, P., Dua, M., & Kumar, A. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-6626-9_23 · Cited by 17 · see citing works