Maximum likelihood linear transformations for HMM-based speech recognition
Integrity signals
Registry
Cites
Statistics
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?
MLLR is a method to re-estimate the means of the Gaussians of an acoustic model so that it maximises the probability of the model to generate a new dataset.
“Given an initial GMM and a new dataset, MLLR is a method to re-estimate the means of the Gaussians of the acoustic model so that it maximises the probability of the model to generate the new dataset.”
From Making Emergency Calls More Accessible to Older Adults Through a Hands-free Speech Interface in the House (2019) · cites this paper as Gales, 1998
Cited by
Sorted by citations
Data Augmentation for Deep Neural Network Acoustic Modeling
Cui, X., Goel, V., & Kingsbury, B. (2015) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2015.2438544 · Cited by 376 · see citing works
Brain-to-text: decoding spoken phrases from phone representations in the brain
Herff, C., Heger, D., de Pesters, A., Telaar, D., Brunner, P., Schalk, G., & Schultz, T. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00217 · Cited by 297 · see citing works
Swietojanski, P., & Renals, S. (2014) · 2014 IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2014.7078569 · Cited by 218 · see citing works
Interacting with computers by voice: automatic speech recognition and synthesis
O'Shaughnessy, D. (2003) · Proceedings of the IEEE · doi:10.1109/jproc.2003.817117 · Cited by 132 · see citing works
Learning Robot Manipulation Tasks With Task-Parameterized Semitied Hidden Semi-Markov Model
Tanwani, A. K., & Calinon, S. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2517825 · Cited by 84 · see citing works
Yamagishi, J., Usabaev, B., King, S., Watts, O., Dines, J., Tian, J., Guan, Y., Hu, R., Oura, K., Wu, Y.-J., et al. (2010) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2010.2045237 · Cited by 78 · see citing works
Speech Recognition in Alzheimer’s Disease and in its Assessment
Zhou, L., Fraser, K. C., & Rudzicz, F. (2016) · Interspeech 2016 · doi:10.21437/interspeech.2016-1228 · Cited by 62 · see citing works
Model adaptation and adaptive training for the recognition of dysarthric speech
Sehgal, S., & Cunningham, S. (2015) · Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies · doi:10.18653/v1/w15-5112 · Cited by 47 · see citing works
Geng, M., Xie, X., Ye, Z., Wang, T., Li, G., Hu, S., Liu, X., & Meng, H. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3195113 · Cited by 48 · see citing works
Bhat, C., Vachhani, B., & Kopparapu, S. (2016) · Interspeech 2016 · doi:10.21437/interspeech.2016-1085 · Cited by 20 · see citing works
Vacher, M., Aman, F., Rossato, S., Portet, F., & Lecouteux, B. (2019) · ACM Transactions on Accessible Computing · doi:10.1145/3310132 · Cited by 10 · see citing works
Neural Architecture Search for LF-MMI Trained Time Delay Neural Networks
Hu, S., Xie, X., Liu, S., Cui, M., Geng, M., Liu, X., & Meng, H. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9413630 · Cited by 6 · see citing works
- Authors
- Gales, M. J. F.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Gales, M.J.F.
- ORCID
- Gales, M.J.F.
- Venue
- Computer Speech & Language
- Publisher
- Elsevier BV
- Published
- 1998-04
- Volume
- 12
- Issue
- 2
- Pages
- 75-98
- ISSN
- 0885-2308 (ISSN-L)
- OpenAlex
- W2002342963
- Cited by
- 1,482
- References
- 0
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 9 · 2023: 15 · 2022: 30 · 2021: 30 · 2020: 40 · 2019: 57 · 2018: 66 · 2017: 91 · 2016: 103 · 2015: 108 · 2014: 116 · 2013: 111 · 2012: 87
- FWCI
- 15.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Speech & Language (publishedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- 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, speaker adaptation, environmental adaptation, re-estimation, speaker adaptive training, incremental adaptation, computational cost
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
