The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Makalah ini menunjukkan bagaimana algoritma pengelompokan keadaan pembahagian yang menjana model Markov Tersembunyi (HMM) akustik boleh mendapat manfaat daripada perwakilan campuran terikat bagi fungsi ketumpatan kebarangkalian (pdf) sesuatu keadaan dan meningkatkan prestasi pengecaman. Algoritma pengelompokan berasaskan pokok keputusan yang popular, seperti contohnya algoritma Pemisahan Negeri Berturut-turut (SSS) menggunakan penyederhanaan apabila mengelompokkan data. Mereka mewakili negeri menggunakan satu pdf Gaussian. Kami menunjukkan bahawa anggaran pdf sebenar oleh Gaussian tunggal ini terlalu kasar, contohnya Gaussian tunggal tidak boleh mewakili perbezaan bahagian simetri pdf keadaan hipotesis baharu yang dijana semasa menilai keuntungan pecahan keadaan (yang akan menentukan perpecahan negeri). Penggunaan perwakilan yang lebih canggih akan membawa kepada masalah pengiraan yang sukar diatasi yang kami selesaikan dengan menggunakan perwakilan pdf campuran terikat. Selain itu, kami mengekang buku kod untuk tidak berubah semasa perpecahan. Di antara pemisahan keadaan, kekangan ini dilonggarkan dan buku kod dikemas kini. Dalam makalah ini, kami mencadangkan pelanjutan kepada algoritma SSS, yang dipanggil algoritma Pemisahan Negeri Berturutan Campuran Terikat (TM-SSS). TM-SSS menunjukkan sehingga kira-kira 31% pengurangan ralat berbanding dengan algoritma Pemisahan Keadaan Berturut-turut Maksimum (ML-SSS) untuk percubaan pengecaman perkataan.
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Salinan
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
Salinan
@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},}
Salinan
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 -