The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
A study on speaker adaptation of the parameters of continuous density hidden Markov models
Abstract
For a speech-recognition system based on continuous-density hidden Markov models (CDHMM), speaker adaptation of the parameters of CDHMM is formulated as a Bayesian learning procedure. A speaker adaptation procedure which is easily integrated into the segmental k-means training procedure for obtaining adaptive estimates of the CDHMM parameters is presented. Some results for adapting both the mean and the diagonal covariance matrix of the Gaussian state observation densities of a CDHMM are reported. The results from tests on a 39-word English alpha-digit vocabulary in isolated word mode indicate that the speaker adaptation procedure achieves the same level of performance as that of a speaker-independent system, when one training token from each word is used to perform speaker adaptation. It shows that much better performance is achieved when two or more training tokens are used for speaker adaptation. When compared with the speaker-dependent system, it is found that the performance of speaker adaptation is always equal to or better than that of speaker-dependent training using the same amount of training data.>
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Cites
An introduction to hidden Markov models
Rabiner, L., & Juang, B. (1986) · IEEE ASSP Magazine · doi:10.1109/massp.1986.1165342 · Cited by 4,775
Self-citationPattern Classification and Scene Analysis
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On the use of instantaneous and transitional spectral information in speaker recognition
Soong, F. K., & Rosenberg, A. E. (1988) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/29.1598 · Cited by 270
On the use of bandpass liftering in speech recognition
Juang, B.-H., Rabiner, L., & Wilpon, J. (1987) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1987.1165237 · Cited by 238
Self-citationMaximum-Likelihood Estimation for Mixture Multivariate Stochastic Observations of Markov Chains
Juang, B.-H. (1985) · AT&T Technical Journal · doi:10.1002/j.1538-7305.1985.tb00273.x · Cited by 236
Self-citationA Segmental k-Means Training Procedure for Connected Word Recognition
Rabiner, L. R., Wilpon, J. G., & Juang, B.-H. (1986) · AT&T Technical Journal · doi:10.1002/j.1538-7305.1986.tb00368.x · Cited by 218
Self-citationSpeaker adaptation through vector quantization
Shikano, K., Lee, K.-F., & Reddy, R. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1986.1168676 · Cited by 94
Unsupervised speaker adaptation method based on hierarchical spectral clustering
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Cited by
Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains
Gauvain, J.-L., & Lee, C.-H. (1994) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.279278 · Cited by 2,108
Self-citationSpeech recognition in noisy environments: A survey
Gong, Y. (1995) · Speech Communication · doi:10.1016/0167-6393(94)00059-j · Cited by 635
Automatic speech recognition and speech variability: A review
Benzeghiba, M., De Mori, R., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., Fissore, L., Laface, P., Mertins, A., Ris, C., et al. (2007) · Speech Communication · doi:10.1016/j.specom.2007.02.006 · Cited by 529
Speaker adaptation using constrained estimation of Gaussian mixtures
Digalakis, V. V., Rtischev, D., & Neumeyer, L. G. (1995) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.466659 · Cited by 377
A frequency warping approach to speaker normalization
Lee, L., & Rose, R. (1998) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.650310 · Cited by 294
Metadata
- Authors
- Lee, C.-H., Lin, C.-H., & Juang, B.-H.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 1991-04
- Volume
- 39
- Issue
- 4
- Pages
- 806-814
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2104687512
- Cited by
- 262
- References
- 11
- FWCI
- 4.60
- Type
- journal article
- Language
- en
- Open access
- closed
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