The Experts below are selected from a list of 34602 Experts worldwide ranked by ideXlab platform
W Reichl - One of the best experts on this subject based on the ideXlab platform.
-
decision tree state tying based on penalized Bayesian Information Criterion
International Conference on Acoustics Speech and Signal Processing, 1999Co-Authors: Wu Chou, W ReichlAbstract:In this paper, an approach of the penalized Bayesian Information Criterion (pBIC) for decision tree state tying is described. The pBIC is applied to two important applications. First, it is used as a decision tree growing Criterion in place of the conventional approach of using a heuristic constant threshold. It is found that original BIC penalty is too low and will not lead to a compact decision tree state tying model. Based on Wolfe's modification to the asymptotic null distribution, it is derived that two times BIC penalty should be used for decision tree state tying based on pBIC. Secondly, pBIC is studied as a model compression Criterion for decision tree state tying based acoustic modeling. Experimental results on a large vocabulary (Wall Street Journal) speech recognition task indicate that a compact decision tree could be achieved with almost no loss of the speech recognition performance.
-
ICASSP - Decision tree state tying based on penalized Bayesian Information Criterion
1999 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999Co-Authors: Wu Chou, W ReichlAbstract:In this paper, an approach of the penalized Bayesian Information Criterion (pBIC) for decision tree state tying is described. The pBIC is applied to two important applications. First, it is used as a decision tree growing Criterion in place of the conventional approach of using a heuristic constant threshold. It is found that original BIC penalty is too low and will not lead to a compact decision tree state tying model. Based on Wolfe's modification to the asymptotic null distribution, it is derived that two times BIC penalty should be used for decision tree state tying based on pBIC. Secondly, pBIC is studied as a model compression Criterion for decision tree state tying based acoustic modeling. Experimental results on a large vocabulary (Wall Street Journal) speech recognition task indicate that a compact decision tree could be achieved with almost no loss of the speech recognition performance.
Toru Imai - One of the best experts on this subject based on the ideXlab platform.
-
On the overestimation of widely applicable Bayesian Information Criterion
arXiv: Methodology, 2019Co-Authors: Toru ImaiAbstract:A widely applicable Bayesian Information Criterion (Watanabe, 2013) is applicable for both regular and singular models in the model selection problem. This Criterion tends to overestimate the log marginal likelihood. We identify an overestimating term of a widely applicable Bayesian Information Criterion. Adjustment of the term gives an asymptotically unbiased estimator of the leading two terms of asymptotic expansion of the log marginal likelihood. In numerical experiments on regular and singular models, the adjustment resulted in smaller bias than the original Criterion.
-
low latency speaker diarization based on Bayesian Information Criterion with multiple phoneme classes
International Conference on Acoustics Speech and Signal Processing, 2012Co-Authors: Takahiro Oku, Shoei Sato, Akio Kobayashi, Shinichi Homma, Toru ImaiAbstract:Low-latency speaker diarization is desirable for online-oriented speaker adaptation in real-time speech recognition. Especially in spontaneous conversations, several speakers tend to speak alternatively and continuously without any silence in between utterances. We therefore propose a speaker diarization method that detects speaker-change points and determines the speaker with a fixed low latency on the basis of a Bayesian Information Criterion (BIC) by using acoustic features classified into multiple phoneme classes. To improve the accuracy of speaker diarization in the low latency condition, the speaker-decision is made continuously at each phoneme boundary. In an experiment on conversational broadcast news programs, our diarization method reduced the speaker diarization error rate relatively by 20.0% compared to the conventional BIC with a single phoneme class. The online speaker adaptation applied in a speech-recognition experiment reduced word error rate at speaker-change points relatively by 7.8%.
-
ICASSP - Low-latency speaker diarization based on Bayesian Information Criterion with multiple phoneme classes
2012 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2012Co-Authors: Takahiro Oku, Shoei Sato, Akio Kobayashi, Shinichi Homma, Toru ImaiAbstract:Low-latency speaker diarization is desirable for online-oriented speaker adaptation in real-time speech recognition. Especially in spontaneous conversations, several speakers tend to speak alternatively and continuously without any silence in between utterances. We therefore propose a speaker diarization method that detects speaker-change points and determines the speaker with a fixed low latency on the basis of a Bayesian Information Criterion (BIC) by using acoustic features classified into multiple phoneme classes. To improve the accuracy of speaker diarization in the low latency condition, the speaker-decision is made continuously at each phoneme boundary. In an experiment on conversational broadcast news programs, our diarization method reduced the speaker diarization error rate relatively by 20.0% compared to the conventional BIC with a single phoneme class. The online speaker adaptation applied in a speech-recognition experiment reduced word error rate at speaker-change points relatively by 7.8%.
Tatsuya Kawahara - One of the best experts on this subject based on the ideXlab platform.
-
unsupervised speaker indexing using speaker model selection based on Bayesian Information Criterion
International Conference on Acoustics Speech and Signal Processing, 2003Co-Authors: Masafumi Nishida, Tatsuya KawaharaAbstract:The paper addresses unsupervised speaker indexing for discussion audio archives. In discussions, the speaker changes frequently, thus the duration of utterances is very short and its variation is large, which causes significant problems in applying conventional methods such as model adaptation and variance-BIC (Bayesian Information Criterion) methods. We propose a flexible framework that selects an optimal speaker model (GMM or VQ) based on the BIC according to the duration of utterances. When the speech segment is short, the simple and robust VQ-based method is expected to be chosen, while GMM can be reliably trained for long segments. For a discussion archive having a total duration of 10 hours, it is demonstrated that the proposed method achieves higher indexing performance than that of conventional methods.
-
INTERSPEECH - Speaker model selection using Bayesian Information Criterion for speaker indexing and speaker adaptation.
2003Co-Authors: Masafumi Nishida, Tatsuya KawaharaAbstract:This paper addresses unsupervised speaker indexing for discussion audio archives. We propose a flexible framework that selects an optimal speaker model (GMM or VQ) based on the Bayesian Information Criterion (BIC) according to input utterances. The framework makes it possible to use a discrete model whenthedataissparse, andtoseamlesslyswitchtoacontinuous model after a large cluster is obtained. The speaker indexing is also applied and evaluated at automatic speech recognition of discussions by adapting a speaker-independent acoustic model to each participant. It is demonstrated that indexing with our method is sufficiently accurate for the speaker adaptation.
-
ICASSP (1) - Unsupervised speaker indexing using speaker model selection based on Bayesian Information Criterion
2003 IEEE International Conference on Acoustics Speech and Signal Processing 2003. Proceedings. (ICASSP '03)., 1Co-Authors: Masafumi Nishida, Tatsuya KawaharaAbstract:The paper addresses unsupervised speaker indexing for discussion audio archives. In discussions, the speaker changes frequently, thus the duration of utterances is very short and its variation is large, which causes significant problems in applying conventional methods such as model adaptation and variance-BIC (Bayesian Information Criterion) methods. We propose a flexible framework that selects an optimal speaker model (GMM or VQ) based on the BIC according to the duration of utterances. When the speech segment is short, the simple and robust VQ-based method is expected to be chosen, while GMM can be reliably trained for long segments. For a discussion archive having a total duration of 10 hours, it is demonstrated that the proposed method achieves higher indexing performance than that of conventional methods.
Wu Chou - One of the best experts on this subject based on the ideXlab platform.
-
decision tree state tying based on penalized Bayesian Information Criterion
International Conference on Acoustics Speech and Signal Processing, 1999Co-Authors: Wu Chou, W ReichlAbstract:In this paper, an approach of the penalized Bayesian Information Criterion (pBIC) for decision tree state tying is described. The pBIC is applied to two important applications. First, it is used as a decision tree growing Criterion in place of the conventional approach of using a heuristic constant threshold. It is found that original BIC penalty is too low and will not lead to a compact decision tree state tying model. Based on Wolfe's modification to the asymptotic null distribution, it is derived that two times BIC penalty should be used for decision tree state tying based on pBIC. Secondly, pBIC is studied as a model compression Criterion for decision tree state tying based acoustic modeling. Experimental results on a large vocabulary (Wall Street Journal) speech recognition task indicate that a compact decision tree could be achieved with almost no loss of the speech recognition performance.
-
ICASSP - Decision tree state tying based on penalized Bayesian Information Criterion
1999 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999Co-Authors: Wu Chou, W ReichlAbstract:In this paper, an approach of the penalized Bayesian Information Criterion (pBIC) for decision tree state tying is described. The pBIC is applied to two important applications. First, it is used as a decision tree growing Criterion in place of the conventional approach of using a heuristic constant threshold. It is found that original BIC penalty is too low and will not lead to a compact decision tree state tying model. Based on Wolfe's modification to the asymptotic null distribution, it is derived that two times BIC penalty should be used for decision tree state tying based on pBIC. Secondly, pBIC is studied as a model compression Criterion for decision tree state tying based acoustic modeling. Experimental results on a large vocabulary (Wall Street Journal) speech recognition task indicate that a compact decision tree could be achieved with almost no loss of the speech recognition performance.
Masafumi Nishida - One of the best experts on this subject based on the ideXlab platform.
-
unsupervised speaker indexing using speaker model selection based on Bayesian Information Criterion
International Conference on Acoustics Speech and Signal Processing, 2003Co-Authors: Masafumi Nishida, Tatsuya KawaharaAbstract:The paper addresses unsupervised speaker indexing for discussion audio archives. In discussions, the speaker changes frequently, thus the duration of utterances is very short and its variation is large, which causes significant problems in applying conventional methods such as model adaptation and variance-BIC (Bayesian Information Criterion) methods. We propose a flexible framework that selects an optimal speaker model (GMM or VQ) based on the BIC according to the duration of utterances. When the speech segment is short, the simple and robust VQ-based method is expected to be chosen, while GMM can be reliably trained for long segments. For a discussion archive having a total duration of 10 hours, it is demonstrated that the proposed method achieves higher indexing performance than that of conventional methods.
-
INTERSPEECH - Speaker model selection using Bayesian Information Criterion for speaker indexing and speaker adaptation.
2003Co-Authors: Masafumi Nishida, Tatsuya KawaharaAbstract:This paper addresses unsupervised speaker indexing for discussion audio archives. We propose a flexible framework that selects an optimal speaker model (GMM or VQ) based on the Bayesian Information Criterion (BIC) according to input utterances. The framework makes it possible to use a discrete model whenthedataissparse, andtoseamlesslyswitchtoacontinuous model after a large cluster is obtained. The speaker indexing is also applied and evaluated at automatic speech recognition of discussions by adapting a speaker-independent acoustic model to each participant. It is demonstrated that indexing with our method is sufficiently accurate for the speaker adaptation.
-
ICASSP (1) - Unsupervised speaker indexing using speaker model selection based on Bayesian Information Criterion
2003 IEEE International Conference on Acoustics Speech and Signal Processing 2003. Proceedings. (ICASSP '03)., 1Co-Authors: Masafumi Nishida, Tatsuya KawaharaAbstract:The paper addresses unsupervised speaker indexing for discussion audio archives. In discussions, the speaker changes frequently, thus the duration of utterances is very short and its variation is large, which causes significant problems in applying conventional methods such as model adaptation and variance-BIC (Bayesian Information Criterion) methods. We propose a flexible framework that selects an optimal speaker model (GMM or VQ) based on the BIC according to the duration of utterances. When the speech segment is short, the simple and robust VQ-based method is expected to be chosen, while GMM can be reliably trained for long segments. For a discussion archive having a total duration of 10 hours, it is demonstrated that the proposed method achieves higher indexing performance than that of conventional methods.
