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 801–817 of 817
Ashwini, P., Bharathi, S. H., & Raghavendra, M. K. (2023) · International Conference on Advances in Electronics Computers and Communications · doi:10.1109/icaecc59324.2023.10560190
Globally Normalizing the Transducer for Streaming Speech Recognition
van Dalen, R. C. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890301
Venkatesh, D. G. K., Saran Karky, S., & Aaron Vineeth, A. (2025) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-28842
A Voice Cloning Detection: Distinguishing Real from Mimicked Voices Using SVM
Sharada, H. N., Allagi, S., Sandhya, S., V, Preethi, P., Rodriguez-Baca, L. S., & de la Vega, C. F. C. P. (2025) · International Conference on Computing Technologies (ICOCT) · doi:10.1109/icoct64433.2025.11118654
Vocalization-based Fine-grained Prediction of Giant Panda Ovulation Time
Tang, J., Zhao, Q., Chen, P., Hou, R., Liu, T., Pan, F., Sun, W., He, M., & Wu, P. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228335
A Noise-Robust End-to-End Framework for Amharic Speech Recognition
Ejigu, Y. A., Asfaw, T. T., Tadesse, S. A., Tegegne, A. Y., & Chekole, A. K. (2025) · International Conference on Information and Communication Technology for Development for Africa (ICT4DA) · doi:10.1109/ict4da67218.2025.11282733
Acoustic Feature Transformation Based on Generalized Criteria for Speech Recognition
坂井, 誠., & Sakai, M. (2010) · Institutional Repositories DataBase (IRDB)
Deep neural networks for source separation and noise-robust speech recognition
Nugraha, A. A. (2017) · theses.fr (ABES) · doi:10.70675/3491cabazda49z4945zbaf4z883f0ecfe928
Granger, N. (2019) · theses.fr (ABES) · doi:10.70675/9f7530bez3094z498fzbaf7zbfac62e25c00
Scheytt, J., Osmers, E., Litvin, L., & Kolossa, D. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-21582-6_14
Discriminative Boosting Algorithm for Diversified Front-End Phonotactic Language Recognition
Liu, W., Cai, M., Zhang, W.-Q., Liu, J., & Johnson, M. T. (2015) · Journal of Signal Processing Systems · doi:10.1007/s11265-015-1017-1
The dependence of feature vectors under adverse noise
Shieh, W.-C., & Chang, S.-C. (1999) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1999-608
Parallel training of DNNs with Natural Gradient and Parameter Averaging
Povey, D., Zhang, X., & Khudanpur, S. (2014) · arXiv · doi:10.48550/arxiv.1410.7455 · Cited by 148
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
