The DOI matches its Crossref record; 335 of the 338 references it cites were found in a registry or an index.
Automatic speech recognition and speech variability: A review
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Previous work has shown that TTR and CD are expected to increase or remain constant as healthy individuals age [74], [75], [76].”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [75]
Citations
Cites
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Haykin, S. (1986) · Cited by 12,603
Introduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,058
Fundamentals of speech recognition
Rabiner, L. R., & Juang, B.-H. F. (1993) · Cited by 7,680
Davis, S., & Mermelstein, P. (1980) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1980.1163420 · Cited by 5,465
Linear prediction: A tutorial review
Makhoul, J. (1975) · Proceedings of the IEEE · doi:10.1109/proc.1975.9792 · Cited by 4,047
Introduction to statistical pattern recognition (2nd ed.)
Fukunaga, K. (1990) · Academic Press Professional, Inc. eBooks · Cited by 3,802
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
Wittenmark, B. (1993) · Automatica · doi:10.1016/0005-1098(93)90162-m · Cited by 3,067
Acoustic Theory of Speech Production
Fant, G. (1971) · De Gruyter eBooks · doi:10.1515/9783110873429 · Cited by 2,966
Flagged first, then by citations
3 of the 338 references couldn’t be matched to a known paper.
Research works printed but not linked: 15
- Beauford, J.Q., 1999. Compensating for variation in speaking rate, PhD thesis, Electrical Engineering, University of Pittsburgh.
- Bou-Ghazale, S.E., Hansen, J.L.H., 1995. Improving recognition and synthesis of stressed speech via feature perturbation in a source generator framework. In: ECSA-NATO Proceedings Speech Under Stress Workshop, Lisbon, Portugal, pp. 45–48.
- Das, S., Lubensky, D., Wu, C., 1999. Towards robust speech recognition in the telephony network environment – cellular and landline conditions. In: Proceedings of Eurospeech, Budapest, Hungary, pp. 1959–1962. · DOI
- Eide, E., Gish, H., 1996. A parametric approach to vocal tract length normalization. In: Proceedings of ICASSP, Atlanta, GA, pp. 346–349. · DOI
- ESCA Workshop on Modeling Pronunciation Variation for Automatic Speech Recognition, 1998.
- Eskenazi, M., Pelton, G., 2002. Pinpointing pronunciation errors in children speech: examining the role of the speech recognizer. In: Proceedings of the PMLA Workshop, Colorado, USA.
- Gales, M.J.F., 2001. Acoustic factorization. In: Proceedings of ASRU, Madona di Campiglio, Italy.
- Hansen, 1995, A source generator framework for analysis of acoustic correlates of speech under stress. part i: pitch, duration, and intensity effects, The Journal of the Acoustical Society of America
- Iivonen, A., Harinen, K., Keinanen, L., Kirjavainen, J., Meister, E., Tuuri, L., 2003. Development of a multiparametric speaker profile for speaker recognition. In: Proceedings of ICPhS, Barcelona, Spain, pp. 695–698.
- ISCA Tutorial and Research Workshop, 2002. Pronunciation Modeling and Lexicon Adaptation for Spoken Language Technology (PMLA-2002).
- Lombard, 1911, Le signe de l’élévation de la voix, Ann. Maladies Oreille, Larynx, Nez, Pharynx, 37
- Leonardo Neumeyer, Horacio Franco, Mitchel Weintraub, and Patti Price. 1996. Automatic text-independent pronunciation scoring of foreign language student speech. In: Proceedings of ICSLP, Philadelphia, PA, pp. 1457–1460. · DOI
- Selouani, S.-A., Tolba, H., O’Shaughnessy, D., 2002. Distinctive features, formants and cepstral coefficients to improve automatic speech recognition. In: Conference on Signal Processing, Pattern Recognition and Applications, IASTED. Crete, Greece, pp. 530–535.
- Uebler, U., Boros, M., 1999. Recognition of non-native german speech with multilingual recognizers. In: Proceedings of Eurospeech, vol. 2. Budapest, Hungary, pp. 911–914. · DOI
- Mitch Weintraub, Kelsey Taussig, Kate Hunicke-Smith, Amy Snodgrass. 1996. Effect of speaking style on LVCSR performance. In: Proceedings Addendum of ICSLP, Philadelphia, PA, USA.
Cited by
Sorted by citations
Items 501–528 of 528
Enhancing Email Usability with Speech and NLP for Blind and Visually Impaired Users
Rani, K., Verma, L., Grover, A., Saini, H., & Satapathy, O. (2025) · International Conference on Computational Intelligence and Communication Technologies (CCICT) · doi:10.1109/ccict65753.2025.00104
Domain Knowledge and Language Embeddings for Low-Resource Multilingual Phoneme ASR
Legchenko, A., & Bondarenko, I. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-07959-6_10
ConCaV: Confusion-triggered Captioning for Video Calls
Heck, M., Bunz, A., Kadri, O. A., Alkadre, M., Omar, R., Blaauw, R., & Becker, C. (2025) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3715070.3749225
Khadar, K. V. A., Kumar, R., & Sameer, V. V. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-05855-3_1
Decoding child speech in silence and noise: The type of background noise shapes adults’ processing
Samimifar, M., & Bulgarelli, F. (2025) · Attention Perception & Psychophysics · doi:10.3758/s13414-025-03194-4
Komatsu, T., Kobayashi, K., Yamada, S., Funakoshi, K., & Nakano, M. (2012) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.27.263
A Keyword Based Interactive Speech Recognition System for Embedded Applications
Ceron, I. F. C., & Badillo, A. G. G. (2011)
Stuttering Disfluency Correction System Using Artificial Intelligence
Aljameel, S. S., Alqahtani, D. A., Algarni, D. A., Aluhideb, D. A., Alamoodi, F. A., Aljafaari, S. F., & Alsafwani, Z. A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-99958-1_7
Evaluating Automatic Speech Recognition Models: How Well Do They Handle Accents?
Liu, W., Xiong, Y., Hu, B., & Kwak, D. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-12930-7_31
Exploring Dynamic Parameters for Vietnamese Gender-Independent ASR
Leang, S., Castelli, E., Vaufreydaz, D., & Sam, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-00972-2_19
Bhowmik, A. K. (2026) · Wiley eBooks · doi:10.1002/9781119615996.ch14
Vigilance towards AI voices can be nudged through a change in ‘MINDSET’
Kirk, N. W. (2026) · Journal of Cybersecurity · doi:10.1093/cybsec/tyag001
Li, X., & Yu, F. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402377
Learning time-dependent data with the signature transform
Fermanian, A. (2021) · theses.fr (ABES) · doi:10.70675/f5cbfff9z7a6dz4662z9cb6z946452d1ad22 · Cited by 3
Zeidan, D. E. B. (2023) · theses.fr (ABES) · doi:10.70675/68d87bdczbe74z443aza16az2ef25f9030ac
Generating diverse synthetic data for ASR training data augmentation
Ogun, S. (2024) · theses.fr (ABES) · doi:10.70675/71c45896z7718z43c4z9f38zac28fa30879c
Exploring GenAI as an Alternative to Traditional Language Learning
Eroglu, U., Oran, Ç., Severdim, K., Çelik, K. K., & Hancı-Azizoğlu, E. B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/407558
Lei, C., Cui, Z., Duan, Y., Wu, Z., Qu, D., Wu, W., Zhang, Z., Ji, J. S., Zhou, B., Wu, J., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102823
Analysis of the effectiveness of the Resemblyzer library for short-command voice authentication
Trusov, M. G., Turuta, O., & Uzlov, D. (2025) · Bulletin of V N Karazin Kharkiv National University series «Mathematical modeling Information technology Automated control systems» · doi:10.26565/2304-6201-2025-68-09
Rizzo, A. ”., Mozgai, S., Hale, B., & Shilling, R. D. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33151-0.00003-4
Collaborative Robot Technologies for Industrial Tasks
Li, W., Zaatari, S. E., Marei, M., Usman, Z., & Pham, D. T. (2026) · Springer series in advanced manufacturing · doi:10.1007/978-3-032-20817-0_2
Development of a Mobile Application for Disordered Polish Speech Recording
Babiński, M., Bączyk, A., Wesołowski, J., & Piernicki, T. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-21376-1_16
Stojcheva, M. M., Petkovski, G., & Mishkovski, I. (2026) · Applied Sciences · doi:10.3390/app16199635
DICTOBOT - autonomiczny agent ze zdolnością komunikacji
Kowalczuk, Z., & Czubenko, M. (2010) · Zeszyty Naukowe Wydziału ETI Politechniki Gdańskiej. Technologie Informacyjne
Cross-Word Pronunciation Variations
AbuZeina, D., & Elshafei, M. (2011) · Springer briefs in electrical and computer engineering · doi:10.1007/978-1-4614-1213-7_4
Metadata
- Authors
- Benzeghiba, M., De Mori, R., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., Fissore, L., Laface, P., Mertins, A., Ris, C., Rose, R., Tyagi, V., & Wellekens, C.
- Affiliations
- Multitel (BE)
- Corresponding author
- Dupont, S.
- Venue
- Speech Communication
- Publisher
- Elsevier BV
- Published
- 2007-10
- Volume
- 49
- Issue
- 10-11
- Pages
- 763-786
- ISSN
- 0167-6393 (ISSN-L)
- OpenAlex
- W2168961642
- Cited by
- 529
- References
- 338
- Citations by year
- 2026: 18 · 2025: 33 · 2024: 40 · 2023: 37 · 2022: 50 · 2021: 33 · 2020: 29 · 2019: 23 · 2018: 25 · 2017: 24 · 2016: 22 · 2015: 33 · 2014: 27 · 2013: 30 · 2012: 34
- FWCI
- 17.51
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Licence
- Locations
- Speech Communication (publishedVersion)
- HAL (submittedVersion, other-oa)
- HAL (submittedVersion)
- PORTO@Iris (submittedVersion)
- ORBi UMONS (submittedVersion)
- PORTO Publications Open Repository TOrino (Politecnico di Torino) (submittedVersion)
- Trinity's Access to Research Output (TARA) (Trinity College Dublin) (submittedVersion)
- Funders
- Sixth Framework Programme (IST-2002-002034)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
- Keywords
- speech recognition, speech variation, robustness, foreign accent, background noise, directory assistance, regional accents, sociolinguistic variation, speaker variability, speech rate, vocal effort, non-stationarity, limited training data, spoken language systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review