The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Maximum mutual information estimation of hidden Markov model parameters for speech recognition
Abstract
A method for estimating the parameters of hidden Markov models of speech is described. Parameter values are chosen to maximize the mutual information between an acoustic observation sequence and the corresponding word sequence. Recognition results are presented comparing this method with maximum likelihood estimation.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Baum, L. E. (1972) · Medical Entomology and Zoology · Cited by 1,405
A Maximum Likelihood Approach to Continuous Speech Recognition
Bahl, L. R., Jelinek, F., & Mercer, R. L. (1983) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1983.4767370 · Cited by 1,220
Self-citationSome experiments with large-vocabulary isolated-word sentence recognition
Bahl, L. R., Das, S. K., de Souza, P., Jelinek, F., Katz, S., Mercer, R. L., & Picheny, M. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1984.1172520 · Cited by 19
Self-citation
Cited by
Sorted by citations
Items 301–400 of 817
Classification of Time-Series Data Using a Generative/Discriminative Hybrid
Abou–Moustafa, K. T., Cheriet, M., & Suen, C. Y. (2004) · International Workshop on Frontiers in Handwriting Recognition (IWFHR) · doi:10.1109/iwfhr.2004.26 · Cited by 15
Recent advancements in automatic speaker authentication
Li, Q., Juang, B.-H. F., Lee, C., Zhou, Q., & Soong, F. K. (1999) · IEEE Robotics & Automation Magazine · doi:10.1109/100.755812 · Cited by 15
Recent Improvement on Maximum Relative Margin Estimation of HMMS for Speech Recognition
Liu, C., Jiang, H., & Rigazio, L. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2006.1660009 · Cited by 15
Robust speech features and acoustic models for speech recognition
Xiao, X. (2009) · doi:10.32657/10356/20733 · Cited by 15
Soft margin estimation for automatic speech recognition
Lee, C., & Li, J. (2008) · Cited by 15
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech RecognitionPreprint
Zhang, Z., Sun, Z., Liu, J., Chen, J., Huo, Z., & Zhang, X. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.07174 · Cited by 18
A Deep Learning Based Chatbot for Campus Psychological TherapyPreprint
Yin, J., Chen, Z., Zhou, K., & Yu, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.06707 · Cited by 15
Clustering-Based Construction of Hidden Markov Models for Generative Kernels
Bicego, M., Cristani, M., Murino, V., Pękalska, E., & Duin, R. P. W. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03641-5_35 · Cited by 14
Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction
Lyu, S. (2011) · Cited by 14
Discriminative training of Gaussian mixture models for large vocabulary speech recognition systems
Bahl, L. R., Padmanabhan, M., Nahamoo, D., & Gopalakrishnan, P. S. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1996.543195 · Cited by 14
Self-citationImproved maximum mutual information estimation training of continuous density HMMs
Zheng, J., Butzberger, J., Franco, H., & Stolcke, A. (2001) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2001-192 · Cited by 14
A study on soft margin estimation for LVCSR
Jin-yu, L., Yan, Z., Lee, C., & Wang, R.-H. (2007) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2007.4430122 · Cited by 14
Cross-Validation EM Training for Robust Parameter Estimation
Shinozaki, T., & Mari Ostendorf, E. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.366943 · Cited by 14
Automata Learning and Stochastic Modeling for Biosequence Analysis
Bejerano, G. (2003) · Cited by 15
RETRACTED ARTICLE: Automatic speech recognition systems: A survey of discriminative techniques
Kaur, A. P., Singh, A., Sachdeva, R., & Kukreja, V. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13645-x · Cited by 13
RetractedRL-Chord: CLSTM-Based Melody Harmonization Using Deep Reinforcement Learning
Ji, S., Yang, X., Luo, J., & Li, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3248793 · Cited by 16
A New Prosody-Assisted Mandarin ASR System
Chen, S., Yang, J., Chiang, C.-Y., Liu, M.-C., & Wang, Y. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2012.2187192 · Cited by 13
Optimizing the Performance of Spoken Language Recognition With Discriminative Training
Zhu, D., Li, H., Ma, B., & Lee, C. (2008) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2008.2005319 · Cited by 13
Multilayer Perceptron Based Hierarchical Acoustic Modeling for Automatic Speech Recognition
Pinto, J. (2010) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-4649 · Cited by 13
Training data selection for improving discriminative training of acoustic models
Liu, S., Chu, F.-H., Lin, S.-H., Lee, H.-S., & Chen, B. (2007) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2007.4430125 · Cited by 13
Minimum word classification error training of HMMS for automatic speech recognition
Yan, Z., Zhu, B., Hu, Y., & Wang, R.-H. (2008) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2008.4518661 · Cited by 13
Aggregate a posteriori linear regression adaptation
Chien, J., & Huang, C. (2006) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tsa.2005.860847 · Cited by 13
Hidden Markov Models and Speaker Adaptation
Schwartz, R. L., & Kubala, F. (1992) · Springer eBooks · doi:10.1007/978-3-642-76626-8_2 · Cited by 13
Matryoshka: A HMM based temporal data clustering methodology for modeling system dynamics
Li, C., Biswas, G., Dale, M. B., & Dale, P. E. R. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6307 · Cited by 13
An improved maximum model distance approach for HMM-based speech recognition systems
He, Q., Kwong, S., Man, K. F., & Tang, K. S. (2000) · Pattern Recognition · doi:10.1016/s0031-3203(99)00144-2 · Cited by 13
Supervision-Guided Codebooks for Masked Prediction in Speech Pre-training
Wang, C., Wang, Y., Wu, Y., Chen, S., Li, J., Liu, S., & Wei, F. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-936 · Cited by 12
Xie, F., Wu, B., Hu, Y., Wang, Y., Jia, G., & Cheng, Y. (2013) · Signal Processing · doi:10.1016/j.sigpro.2013.06.009 · Cited by 12
Image to Modern Chinese Poetry Creation via a Constrained Topic-aware Model
Wu, L., Xu, M. G., Qian, S., & Cui, J. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3381858 · Cited by 13
On Lattice-Free Boosted MMI Training of HMM and CTC-Based Full-Context ASR Models
Zhang, X., Manohar, V., Zhang, D., Zhang, F., Shi, Y., Singhal, N., Chan, J., Peng, F., Saraf, Y., & Seltzer, M. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688056 · Cited by 12
Advances in Large Vocabulary Continuous Speech Recognition
Zweig, G., & Picheny, M. (2004) · Advances in computers · doi:10.1016/s0065-2458(03)60007-0 · Cited by 12
Investigations on sequence training of neural networks
Wiesler, S., Golik, P., Schlüter, R., & Ney, H. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178835 · Cited by 12
Ouivirach, K., & Dailey, M. N. (2010) · International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · Cited by 12
Discriminative training of GMM-HMM acoustic model by RPCL learning
Pang, Z., Tu, S., Su, D., Wu, X. H., & Xu, L. (2011) · Frontiers of Electrical and Electronic Engineering in China · doi:10.1007/s11460-011-0152-0 · Cited by 12
Self-adaptive design of hidden Markov models
Li, J., Wang, J., Zhao, Y., & Yang, Z. (2003) · Pattern Recognition Letters · doi:10.1016/j.patrec.2003.10.001 · Cited by 12
Direct error rate minimization of hidden Markov models
Keshet, J., Cheng, C., Stoehr, M., & McAllester, D. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-172 · Cited by 12
A voice command detection system for aerospace applications
Tabibian, S. (2017) · International Journal of Speech Technology · doi:10.1007/s10772-017-9467-4 · Cited by 12
Discriminative Training for Phonetic Recognition of the Holy Quran
Baig, M. M. A., Qazi, S. A., & Kadri, M. B. (2015) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-015-1693-y · Cited by 12
Shift-Tolerant LVQ and Hybrid LVQ-HMM for Phoneme Recognition
McDermott, E., Iwamida, H., Katagiri, S., & Tohkura, Y. (1990) · Elsevier eBooks · doi:10.1016/b978-0-08-051584-7.50041-3 · Cited by 12
A hybrid syllable recognition system based on vowel spotting
Sirigos, J., Fakotakis, N., & Kokkinakis, G. C. (2002) · Speech Communication · doi:10.1016/s0167-6393(02)00012-2 · Cited by 12
Bias Estimation and Correction in a Classifier using Product of Likelihood-Gaussians
Nagarajan, T., & O’Shaughnessy, D. D. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.366866 · Cited by 12
Speech recognition of mandarin monosyllables
Li, T. F. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(03)00135-3 · Cited by 12
An evolutionary based discriminative system for keyword spotting
Tabibian, S., Akbari, A., & Nasersharif, B. (2011) · CSI International Symposium on Artificial Intelligence and Signal Processing · doi:10.1109/aisp.2011.5960990 · Cited by 12
Discriminatively Trained GMMs for Language Classification Using Boosting Methods
Siu, M.-H., Yang, X., & Gish, H. (2009) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2008.2006653 · Cited by 12
A novel spoken keyword spotting system using support vector machine
Sangeetha, J., & Jothilakshmi, S. (2014) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2014.07.014 · Cited by 12
An HMM/MLP Architecture for Sequence Recognition
Cho, S., & Kim, J. H. (1995) · Neural Computation · doi:10.1162/neco.1995.7.2.358 · Cited by 12
Large Margin Training of Continuous Density Hidden Markov Models
Sha, F., & Saul, L. K. (2009) · doi:10.1002/9780470742044.ch7 · Cited by 12
Improved maximum model distance for HMM training
He, Q., Kwong, S., Man, K. F., & Tang, K. S. (1999) · Electronics Letters · doi:10.1049/el:19990550 · Cited by 12
A discriminant measure for model complexity adaptation
Bahl, L. R., & Padmanabhan, M. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1998.674465 · Cited by 12
Self-citationTowards End-to-End Speech Recognition
Palaz, D. (2016) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-7054 · Cited by 14
Building Acoustic Model Ensembles by Data Sampling With Enhanced Trainings and Features
Chen, X., & Zhao, Y. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2012.2227729 · Cited by 11
Chen, I.-F., Chen, N. F., & Lee, C. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-531 · Cited by 11
Paired Phone-Posteriors Approach to ESL Pronunciation Quality Assessment
Xiao, Y., Soong, F. K., & Hu, W. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1270 · Cited by 11
Tahir, M. A., Huang, H., Zeyer, A., Schlüter, R., & Ney, H. (2019) · Speech Communication · doi:10.1016/j.specom.2019.03.006 · Cited by 11
Probability estimation by feed-forward networks in continuous speech recognition
Renals, S. J., Morgan, N., & Bourlard, H. (2002) · Machine learning for signal processing ... · doi:10.1109/nnsp.1991.239511 · Cited by 11
Matrix updates for perceptron training of continuous density hidden Markov models
Cheng, C., Sha, F., & Saul, L. K. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553394 · Cited by 12
A genetic classification error method for speech recognition
Kwong, S., He, Q., Ku, K. W. C., Chan, T. M., Man, K. F., & Tang, K. S. (2002) · Signal Processing · doi:10.1016/s0165-1684(02)00138-x · Cited by 11
Random Texture Defect Detection Using 1-D Hidden Markov Models Based on Local Binary Patterns
Hadizadeh, H., & Shokouhi, S. B. (2008) · IEICE Transactions on Information and Systems · doi:10.1093/ietisy/e91-d.7.1937 · Cited by 11
Soft margin feature extraction for automatic speech recognition
Li, J., & Lee, C. (2007) · Interspeech (USB) · doi:10.21437/interspeech.2007-5 · Cited by 11
SNR-Adaptive Stream Weighting for Audio-MES ASR
Lee, K.-S. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.921094 · Cited by 11
Multilayer perceptrons for state-dependent weightings of HMM likelihoods
Chung, Y. J., & Un, C. K. (1996) · Speech Communication · doi:10.1016/0167-6393(95)00038-0 · Cited by 11
SAKAI, M., Kitaoka, N., & Nakagawa, S. (2008) · IEICE Transactions on Information and Systems · doi:10.1093/ietisy/e91-d.3.478 · Cited by 11
Study of a Fast Discriminative Training Algorithm for Pattern Recognition
Li, Q. L., & Juang, B.-H. F. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.875992 · Cited by 11
Deterministically annealed design of speech recognizers and its performance on isolated letters
Rao, A. V., Rose, K. H., & Gersho, A. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1998.674467 · Cited by 11
A novel pyramidal-FSMN architecture with lattice-free MMI for speech recognitionPreprint
Yang, X., Li, J., & Zhou, X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.11352 · Cited by 11
Maximizing Mutual Information for TacotronPreprint
Liu, P., Wu, X., Kang, S., Li, G., Su, D., & Yu, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.01145 · Cited by 17
Hidden Markov Models in Dynamic System Modelling and Diagnosis
Al-ani, T. (2011) · InTech eBooks · doi:10.5772/15495 · Cited by 10
Saon, G., & Soltau, H. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854668 · Cited by 10
Training data selection for improving discriminative training of acoustic models
Chen, B., Liu, S., & Chu, F.-H. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.05.009 · Cited by 10
A comparison of training approaches for discriminative segmental models
Tang, H., Gimpel, K., & Livescu, K. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-307 · Cited by 10
A survey on structured discriminative spoken keyword spotting
Tabibian, S. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09739-y · Cited by 10
On Discriminative Parameter Learning of Bayesian Network Classifiers
Pernkopf, F., & Wohlmayr, M. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04174-7_15 · Cited by 10
Keyword spotting using an evolutionary-based classifier and discriminative features
Tabibian, S., Akbari, A., & Nasersharif, B. (2013) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2013.03.009 · Cited by 10
Gaussian Mixture Optimization Based on Efficient Cross-Validation
Shinozaki, T., Furui, S., & Kawahara, T. (2010) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2010.2048235 · Cited by 10
A Rate Distortion Approach for Semi-Supervised Conditional Random Fields
Wang, Y., Haffari, G., Wang, S., & Mori, G. (2009) · Cited by 10
Acoustic Modelling Using Continuous Rational Kernels
Layton, M., & Gales, M. (2007) · The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology · doi:10.1007/s11265-006-0027-4 · Cited by 10
Articulatory features for "meeting" speech recognition
Metze, F. (2006) · Interspeech (USB) · doi:10.21437/interspeech.2006-214 · Cited by 10
Improved speech recognition using a subspace projection approach
Loizou, P. C., & Spanias, A. (1999) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.759044 · Cited by 10
Discriminative n-gram language modeling for Turkish
Arısoy, E., Brian Roark, A., Izhak Shafran, J., & Saraçlar, M. (2008) · Interspeech (USB) · doi:10.21437/interspeech.2008-251 · Cited by 10
GMM-based acoustic modeling for embedded speech recognition
Lévy, C., Linarès, G., & Bonastre, J.-F. (2006) · Interspeech (USB) · doi:10.21437/interspeech.2006-479 · Cited by 10
Low complexity speaker independent command word recognition in car environments
Riis, S. K., & Viikki, O. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2000.862089 · Cited by 10
Training HMMs using a minimum recognition error approach
Franco, H., & Serralheiro, A. (1991) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1991.150350 · Cited by 10
A comparative study of continuous speech recognition using neural networks and hidden Markov models
Renals, S. J., McKelvie, D., & McInnes, F. R. (1991) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1991.150353 · Cited by 10
Discriminative mixture weight estimation for large Gaussian mixture models
Beaufays, F., Weintraub, M., & Konig, Y. (1999) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1999.758131 · Cited by 10
Discriminative codebook design using multiple vector quantization in HMM-based speech recognizers
Peinado, A. M., Segura, J. C., Rubio, A. J., Garcia, P., & Pérez-Córdoba, J. L. (1996) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.486058 · Cited by 10
Competitive training in hidden Markov models (speech recognition)
Young, S. J. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1990.115848 · Cited by 10
Discriminative training methods and their applications to handwriting recognition
Nopsuwanchai, R. (2021) · CL Technical Reports · doi:10.48456/tr-652 · Cited by 10
Bayes risk decoding and its application to system combination
Hoffmeister, B., & Ney, H. (2011) · RWTH Publications (RWTH Aachen) · Cited by 10
Online Learning and Acoustic Feature Adaptation in Large-Margin Hidden Markov Models
Cheng, C., Sha, F., & Saul, L. K. (2010) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2010.2048607 · Cited by 8
Alleviating the over-smoothing problem in GMM-based voice conversion with discriminative training
Hwang, H.-T., Tsao, Y., Wang, H., Wang, Y., & Chen, S. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-668 · Cited by 9
Kubo, Y., Watanabe, S., Nakamura, A., McDermott, E., & Kobayashi, T. (2010) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2010.2076030 · Cited by 9
Automatic Speech Recognition for Real Time Systems
Singh, R., Yadav, H. N., Sharma, M., Gosain, S., & Shah, R. R. (2019) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2019.00-26 · Cited by 9
Two-stage Behavior Cloning for Spoken Dialogue System in Debt Collection
Wang, Z., Liu, J., Cui, H., Jin, C., Yang, M., Wang, Y., Li, X., & Mao, R. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/639 · Cited by 9
Uniform Partitioning of Data Grid for Association Detection
Mousavi, A., & Baraniuk, R. G. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3029487 · Cited by 9
Information Theoretical Kernels for Generative Embeddings Based on Hidden Markov Models
Martins, A. F. T., Bicego, M., Murino, V., Aguiar, P. M. Q., & Figueiredo, M. A. T. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14980-1_45 · Cited by 9
Maximum margin hidden Markov models for sequence classification
Mutsam, N., & Pernkopf, F. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.03.017 · Cited by 9
Discriminative keyword spotting using triphones information and N-best search
Tabibian, S., Akbari, A., & Nasersharif, B. (2017) · Information Sciences · doi:10.1016/j.ins.2017.09.052 · Cited by 9
Extension of a Kernel-Based Classifier for Discriminative Spoken Keyword Spotting
Tabibian, S., Akbari, A., & Nasersharif, B. (2013) · Neural Processing Letters · doi:10.1007/s11063-013-9299-4 · Cited by 9
Maximum mutual information training of distance measures for template based speech recognition
Matton, M., Wachter, M. D., Compernolle, D. V., & Cools, R. F. A. (2005) · Cited by 9
Discriminative Training of Gender-Dependent Acoustic Models
Vaněk, J., Psutka, J., Zelinka, J., Pražák, A., & Psutka, J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04208-9_46 · Cited by 9
A Bayesian classification approach with application to speech recognition
Merhav, N., & Ephraim, Y. (1991) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1991.150393 · Cited by 9
Metadata
- Authors
- Bahl, L., Brown, P., de Souza, P., & Mercer, R.
- Affiliations
- IBM Research - Thomas J. Watson Research Center (US)
- Venue
- ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing
- Published
- 2005-03-24
- Volume
- 11
- Pages
- 49-52
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W1877570817
- Cited by
- 817
- References
- 3
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 13 · 2023: 11 · 2022: 16 · 2021: 30 · 2020: 25 · 2019: 30 · 2018: 33 · 2017: 24 · 2016: 25 · 2015: 34 · 2014: 42 · 2013: 29 · 2012: 39
- FWCI
- 28.55
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- 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
- hidden Markov models, parameter estimation, mutual information maximization, speech recognition, word sequences, maximum likelihood estimation comparison
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