Abstract
In this paper, we apply Variational Bayesian Estimation and Clustering for speech recognition (VBEC) to an acoustic model adaptation. VBEC can estimate parameter posteriors even when a model includes hidden variables, by using Variational Bayesian approach. In addition, VBEC can select an appropriate model structure in clustering triphone states, according to the amount of available adaptation data. Unlike a conventional Bayesian method such as Maximum A Posteriori (MAP), VBEC is useful even in the case of small amounts of data, because the amount of data per one Gaussian increases due to the model structure selection, and over-training is suppressed. We conduct an off-line supervised adaptation experiment on isolated word recognition, and show the advantage of the proposed method over the conventional method, especially when dealing with small amounts of adaptation data.
| Original language | English |
|---|---|
| Pages (from-to) | 568-571 |
| Number of pages | 4 |
| Journal | Unknown Journal |
| Volume | 1 |
| Publication status | Published - 2003 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Signal Processing
- Acoustics and Ultrasonics
Cite this
Application of variational Bayesian estimation and clustering to acoustic model adaptation. / Watanabe, Shinji; Minami, Yasuhiro; Nakamura, Atsushi; Ueda, Naonori.
In: Unknown Journal, Vol. 1, 2003, p. 568-571.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Application of variational Bayesian estimation and clustering to acoustic model adaptation
AU - Watanabe, Shinji
AU - Minami, Yasuhiro
AU - Nakamura, Atsushi
AU - Ueda, Naonori
PY - 2003
Y1 - 2003
N2 - In this paper, we apply Variational Bayesian Estimation and Clustering for speech recognition (VBEC) to an acoustic model adaptation. VBEC can estimate parameter posteriors even when a model includes hidden variables, by using Variational Bayesian approach. In addition, VBEC can select an appropriate model structure in clustering triphone states, according to the amount of available adaptation data. Unlike a conventional Bayesian method such as Maximum A Posteriori (MAP), VBEC is useful even in the case of small amounts of data, because the amount of data per one Gaussian increases due to the model structure selection, and over-training is suppressed. We conduct an off-line supervised adaptation experiment on isolated word recognition, and show the advantage of the proposed method over the conventional method, especially when dealing with small amounts of adaptation data.
AB - In this paper, we apply Variational Bayesian Estimation and Clustering for speech recognition (VBEC) to an acoustic model adaptation. VBEC can estimate parameter posteriors even when a model includes hidden variables, by using Variational Bayesian approach. In addition, VBEC can select an appropriate model structure in clustering triphone states, according to the amount of available adaptation data. Unlike a conventional Bayesian method such as Maximum A Posteriori (MAP), VBEC is useful even in the case of small amounts of data, because the amount of data per one Gaussian increases due to the model structure selection, and over-training is suppressed. We conduct an off-line supervised adaptation experiment on isolated word recognition, and show the advantage of the proposed method over the conventional method, especially when dealing with small amounts of adaptation data.
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M3 - Article
AN - SCOPUS:0141591454
VL - 1
SP - 568
EP - 571
JO - Nuclear Physics A
JF - Nuclear Physics A
SN - 0375-9474
ER -