The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Speaker-independent phone recognition using hidden Markov models
Abstract
Hidden Markov modeling is extended to speaker-independent phone recognition. Using multiple codebooks of various linear-predictive-coding (LPC) parameters and discrete hidden Markov models (HMMs) the authors obtain a speaker-independent phone recognition accuracy of 58.8-73.8% on the TIMIT database, depending on the type of acoustic and language models used. In comparison, the performance of expert spectrogram readers is only 69% without use of higher level knowledge. The authors introduce the co-occurrence smoothing algorithm, which enables accurate recognition even with very limited training data. Since the results were evaluated on a standard database, they can be used as benchmarks to evaluate future systems.>
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Cites
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
Viterbi, A. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1054010 · Cited by 6,759
Jacob, M. (1989) · Health Education Journal · doi:10.1177/001789698904800401 · Cited by 3,090
Continuous speech recognition by statistical methods
Jelinek, F. (1976) · Proceedings of the IEEE · doi:10.1109/proc.1976.10159 · Cited by 920
Proakis, J. (1975) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1975.1162707 · Cited by 826
Interpolated estimation of Markov source parameters from sparse data
Jelinek, F. (1980) · Medical Entomology and Zoology · Cited by 823
Perception and Production of Fluent Speech
Cole, R. A. (2016) · Routledge eBooks · doi:10.4324/9781315638935 · Cited by 775
Speaker-independent isolated word recognition using dynamic features of speech spectrum
Furui, S. (1986) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1986.1164788 · Cited by 748
Pattern recognition in practice
Perkins, J. (1980) · Journal of Biomedical Engineering · doi:10.1016/0141-5425(80)90132-6 · Cited by 343
Large-vocabulary speaker-independent continuous speech recognition: the sphinx system
Reddy, R., & Lee, K.-F. (1988) · Cited by 308
Self-citationThe Acoustic-Modeling Problem in Automatic Speech Recognition.
Brown, P. F. (1987) · doi:10.21236/ada188529 · Cited by 248
Cited by
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Supervised Sequence Labelling with Recurrent Neural Networks
Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2 · Cited by 3,120
Convolutional Neural Networks for Speech Recognition
Abdel-Hamid, O., Mohamed, A.-R., Jiang, H., Deng, L., Penn, G., & Yu, D. (2014) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2014.2339736 · Cited by 2,262
Hybrid speech recognition with Deep Bidirectional LSTM
Graves, A., Jaitly, N., & Mohamed, A.-R. (2013) · 2013 IEEE Workshop on Automatic Speech Recognition and Understanding · doi:10.1109/asru.2013.6707742 · Cited by 1,736
Acoustic Modeling Using Deep Belief Networks
Mohamed, A.-R., Dahl, G. E., & Hinton, G. (2012) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2011.2109382 · Cited by 1,727
Metadata
- Authors
- Lee, K.-F., & Hon, H.-W.
- Venue
- IEEE Transactions on Acoustics, Speech, and Signal Processing
- Published
- 1989
- Volume
- 37
- Issue
- 11
- Pages
- 1641-1648
- ISSN
- 0096-3518 (ISSN-L)
- OpenAlex
- W2077804127
- Cited by
- 927
- References
- 18
- FWCI
- 13.83
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
