Dynamic Bayesian Network-Based Acoustic Models Incorporating Speaking Rate Effects

Takahiro SHINOZAKI; Sadaoki FURUI

Dynamic Bayesian Network-Based Acoustic Models Incorporating Speaking Rate Effects

Takahiro SHINOZAKI, Sadaoki FURUI

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One of the most important issues in spontaneous speech recognition is how to cope with the degradation of recognition accuracy due to speaking rate fluctuation within an utterance. This paper proposes an acoustic model for adjusting mixture weights and transition probabilities of the HMM for each frame according to the local speaking rate. The proposed model is implemented along with variants and conventional models using the Bayesian network framework. The proposed model has a hidden variable representing variation of the "mode" of the speaking rate, and its value controls the parameters of the underlying HMM. Model training and maximum probability assignment of the variables are conducted using the EM/GEM and inference algorithms for the Bayesian networks. Utterances from meetings and lectures are used for evaluation where the Bayesian network-based acoustic models are used to rescore the likelihood of the N-best lists. In the experiments, the proposed model indicated consistently higher performance than conventional HMMs and regression HMMs using the same speaking rate information.

Publication: IEICE TRANSACTIONS on Information Vol.E87-D No.10 pp.2339-2347

Publication Date: 2004/10/01

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Type of Manuscript: PAPER

Category: Speech and Hearing

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Takahiro SHINOZAKI, Sadaoki FURUI, "Dynamic Bayesian Network-Based Acoustic Models Incorporating Speaking Rate Effects" in IEICE TRANSACTIONS on Information, vol. E87-D, no. 10, pp. 2339-2347, October 2004, doi: .
Abstract: One of the most important issues in spontaneous speech recognition is how to cope with the degradation of recognition accuracy due to speaking rate fluctuation within an utterance. This paper proposes an acoustic model for adjusting mixture weights and transition probabilities of the HMM for each frame according to the local speaking rate. The proposed model is implemented along with variants and conventional models using the Bayesian network framework. The proposed model has a hidden variable representing variation of the "mode" of the speaking rate, and its value controls the parameters of the underlying HMM. Model training and maximum probability assignment of the variables are conducted using the EM/GEM and inference algorithms for the Bayesian networks. Utterances from meetings and lectures are used for evaluation where the Bayesian network-based acoustic models are used to rescore the likelihood of the N-best lists. In the experiments, the proposed model indicated consistently higher performance than conventional HMMs and regression HMMs using the same speaking rate information.
URL: https://global.ieice.org/en_transactions/information/10.1587/e87-d_10_2339/_p

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@ARTICLE{e87-d_10_2339,
author={Takahiro SHINOZAKI, Sadaoki FURUI, },
journal={IEICE TRANSACTIONS on Information},
title={Dynamic Bayesian Network-Based Acoustic Models Incorporating Speaking Rate Effects},
year={2004},
volume={E87-D},
number={10},
pages={2339-2347},
abstract={One of the most important issues in spontaneous speech recognition is how to cope with the degradation of recognition accuracy due to speaking rate fluctuation within an utterance. This paper proposes an acoustic model for adjusting mixture weights and transition probabilities of the HMM for each frame according to the local speaking rate. The proposed model is implemented along with variants and conventional models using the Bayesian network framework. The proposed model has a hidden variable representing variation of the "mode" of the speaking rate, and its value controls the parameters of the underlying HMM. Model training and maximum probability assignment of the variables are conducted using the EM/GEM and inference algorithms for the Bayesian networks. Utterances from meetings and lectures are used for evaluation where the Bayesian network-based acoustic models are used to rescore the likelihood of the N-best lists. In the experiments, the proposed model indicated consistently higher performance than conventional HMMs and regression HMMs using the same speaking rate information.},
keywords={},
doi={},
ISSN={},
month={October},}

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TY - JOUR
TI - Dynamic Bayesian Network-Based Acoustic Models Incorporating Speaking Rate Effects
T2 - IEICE TRANSACTIONS on Information
SP - 2339
EP - 2347
AU - Takahiro SHINOZAKI
AU - Sadaoki FURUI
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2004
AB - One of the most important issues in spontaneous speech recognition is how to cope with the degradation of recognition accuracy due to speaking rate fluctuation within an utterance. This paper proposes an acoustic model for adjusting mixture weights and transition probabilities of the HMM for each frame according to the local speaking rate. The proposed model is implemented along with variants and conventional models using the Bayesian network framework. The proposed model has a hidden variable representing variation of the "mode" of the speaking rate, and its value controls the parameters of the underlying HMM. Model training and maximum probability assignment of the variables are conducted using the EM/GEM and inference algorithms for the Bayesian networks. Utterances from meetings and lectures are used for evaluation where the Bayesian network-based acoustic models are used to rescore the likelihood of the N-best lists. In the experiments, the proposed model indicated consistently higher performance than conventional HMMs and regression HMMs using the same speaking rate information.
ER -