The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
The Application of Hidden Markov Models in Speech Recognition
Abstract
Hidden Markov Models (HMMs) provide a simple and effective framework for modelling time-varying spectral vector sequences. As a consequence, almost all present day large vocabulary continuous speech recognition (LVCSR) systems are based on HMMs. Whereas the basic principles underlying HMM-based LVCSR are rather straightforward, the approximations and simplifying assumptions involved in a direct implementation of these principles would result in a system which has poor accuracy and unacceptable sensitivity to changes in operating environment. Thus, the practical application of HMMs in modern systems involves considerable sophistication. The aim of this review is first to present the core architecture of a HMM-based LVCSR system and then describe the various refinements which are needed to achieve state-of-the-art performance. These refinements include feature projection, improved covariance modelling, discriminative parameter estimation, adaptation and normalisation, noise compensation and multi-pass system combination. The review concludes with a case study of LVCSR for Broadcast News and Conversation transcription in order to illustrate the techniques described.
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Cites
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A tutorial on hidden Markov models and selected applications in speech recognition
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A decision-theoretic generalization of on-line learning and an application to boosting
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Cited by
A review of deep learning techniques for speech processing
Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101869 · Cited by 389
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
Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243
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
Dialogue Systems and Conversational Agents for Patients with Dementia: The Human–Robot Interaction
Russo, A., D'Onofrio, G., Gangemi, A., Giuliani, F., Mongiovi, M., Ricciardi, F., Greco, F., Cavallo, F., Dario, P., Sancarlo, D., et al. (2019) · Rejuvenation Research · doi:10.1089/rej.2018.2075 · Cited by 47
Metadata
- Authors
- Gales, M., & Young, S.
- Venue
- Foundations and Trends® in Signal Processing
- Publisher
- Emerald
- Published
- 2007
- Volume
- 1
- Issue
- 3
- Pages
- 195-304
- ISSN
- 1932-8346 (ISSN-L)
- 1932-8354
- OpenAlex
- W4205130185
- Cited by
- 496
- References
- 4
- FWCI
- 21.73
- Type
- journal article
- Language
- en
- Open access
- closed
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