IEICE TRANSACTIONS on Information
Maximum Likelihood Successive State Splitting Algorithm for Tied-Mixture HMnet
Summary :
This paper shows how a divisive state clustering algorithm that generates acoustic Hidden Markov models (HMM) can benefit from a tied-mixture representation of the probability density function (pdf) of a state and increase the recognition performance. Popular decision tree based clustering algorithms, like for example the Successive State Splitting algorithm (SSS) make use of a simplification when clustering data. They represent a state using a single Gaussian pdf. We show that this approximation of the true pdf by a single Gaussian is too coarse, for example a single Gaussian cannot represent the differences in the symmetric parts of the pdf's of the new hypothetical states generated when evaluating the state split gain (which will determine the state split). The use of more sophisticated representations would lead to intractable computational problems that we solve by using a tied-mixture pdf representation. Additionally, we constrain the codebook to be immutable during the split. Between state splits, this constraint is relaxed and the codebook is updated. In this paper, we thus propose an extension to the SSS algorithm, the so-called Tied-mixture Successive State Splitting algorithm (TM-SSS). TM-SSS shows up to about 31% error reduction in comparison with Maximum-Likelihood Successive State Split algorithm (ML-SSS) for a word recognition experiment.
- Publication
- IEICE TRANSACTIONS on Information Vol.E83-D No.10 pp.1890-1897
- Publication Date
- 2000/10/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Speech and Hearing
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Alexandre GIRARDI, Harald SINGER, Kiyohiro SHIKANO, Satoshi NAKAMURA, "Maximum Likelihood Successive State Splitting Algorithm for Tied-Mixture HMnet" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 10, pp. 1890-1897, October 2000, doi: .
Abstract: This paper shows how a divisive state clustering algorithm that generates acoustic Hidden Markov models (HMM) can benefit from a tied-mixture representation of the probability density function (pdf) of a state and increase the recognition performance. Popular decision tree based clustering algorithms, like for example the Successive State Splitting algorithm (SSS) make use of a simplification when clustering data. They represent a state using a single Gaussian pdf. We show that this approximation of the true pdf by a single Gaussian is too coarse, for example a single Gaussian cannot represent the differences in the symmetric parts of the pdf's of the new hypothetical states generated when evaluating the state split gain (which will determine the state split). The use of more sophisticated representations would lead to intractable computational problems that we solve by using a tied-mixture pdf representation. Additionally, we constrain the codebook to be immutable during the split. Between state splits, this constraint is relaxed and the codebook is updated. In this paper, we thus propose an extension to the SSS algorithm, the so-called Tied-mixture Successive State Splitting algorithm (TM-SSS). TM-SSS shows up to about 31% error reduction in comparison with Maximum-Likelihood Successive State Split algorithm (ML-SSS) for a word recognition experiment.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_10_1890/_p
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@ARTICLE{e83-d_10_1890,
author={Alexandre GIRARDI, Harald SINGER, Kiyohiro SHIKANO, Satoshi NAKAMURA, },
journal={IEICE TRANSACTIONS on Information},
title={Maximum Likelihood Successive State Splitting Algorithm for Tied-Mixture HMnet},
year={2000},
volume={E83-D},
number={10},
pages={1890-1897},
abstract={This paper shows how a divisive state clustering algorithm that generates acoustic Hidden Markov models (HMM) can benefit from a tied-mixture representation of the probability density function (pdf) of a state and increase the recognition performance. Popular decision tree based clustering algorithms, like for example the Successive State Splitting algorithm (SSS) make use of a simplification when clustering data. They represent a state using a single Gaussian pdf. We show that this approximation of the true pdf by a single Gaussian is too coarse, for example a single Gaussian cannot represent the differences in the symmetric parts of the pdf's of the new hypothetical states generated when evaluating the state split gain (which will determine the state split). The use of more sophisticated representations would lead to intractable computational problems that we solve by using a tied-mixture pdf representation. Additionally, we constrain the codebook to be immutable during the split. Between state splits, this constraint is relaxed and the codebook is updated. In this paper, we thus propose an extension to the SSS algorithm, the so-called Tied-mixture Successive State Splitting algorithm (TM-SSS). TM-SSS shows up to about 31% error reduction in comparison with Maximum-Likelihood Successive State Split algorithm (ML-SSS) for a word recognition experiment.},
keywords={},
doi={},
ISSN={},
month={October},}
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TY - JOUR
TI - Maximum Likelihood Successive State Splitting Algorithm for Tied-Mixture HMnet
T2 - IEICE TRANSACTIONS on Information
SP - 1890
EP - 1897
AU - Alexandre GIRARDI
AU - Harald SINGER
AU - Kiyohiro SHIKANO
AU - Satoshi NAKAMURA
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2000
AB - This paper shows how a divisive state clustering algorithm that generates acoustic Hidden Markov models (HMM) can benefit from a tied-mixture representation of the probability density function (pdf) of a state and increase the recognition performance. Popular decision tree based clustering algorithms, like for example the Successive State Splitting algorithm (SSS) make use of a simplification when clustering data. They represent a state using a single Gaussian pdf. We show that this approximation of the true pdf by a single Gaussian is too coarse, for example a single Gaussian cannot represent the differences in the symmetric parts of the pdf's of the new hypothetical states generated when evaluating the state split gain (which will determine the state split). The use of more sophisticated representations would lead to intractable computational problems that we solve by using a tied-mixture pdf representation. Additionally, we constrain the codebook to be immutable during the split. Between state splits, this constraint is relaxed and the codebook is updated. In this paper, we thus propose an extension to the SSS algorithm, the so-called Tied-mixture Successive State Splitting algorithm (TM-SSS). TM-SSS shows up to about 31% error reduction in comparison with Maximum-Likelihood Successive State Split algorithm (ML-SSS) for a word recognition experiment.
ER -
English
