An Algorithm for Parameters Estimation of Autoregressive Model of Basic Speech Units

<p>The article considers the problem of estimating autoregressive model parameters of elementary speech units such as phonemes. It is suggested an iterative algorithm based on the Newton numerical minimization technique to search an autoregressive model of phonemes specified its multiple sampl...

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Main Author: I. V. Gubochkin
Format: Article
Language:English
Published: Yaroslavl State University 2013-01-01
Series:Моделирование и анализ информационных систем
Subjects:
Online Access:http://mais-journal.ru/jour/article/view/203
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author I. V. Gubochkin
author_facet I. V. Gubochkin
author_sort I. V. Gubochkin
collection DOAJ
description <p>The article considers the problem of estimating autoregressive model parameters of elementary speech units such as phonemes. It is suggested an iterative algorithm based on the Newton numerical minimization technique to search an autoregressive model of phonemes specified its multiple samples. For this purpose the analytical expressions of the gradient and the Hessian of Kullback–Leibler information divergence between autoregressive models were computed. Experimental studies on a set of English phonemes showed that the developed algorithm requires less computational effort for large amounts of data, and iterations count depends little on the amount of input data as opposed to reference phoneme selection algorithm based on the criterion of a minimum sum of information divergence. Moreover, the proposed algorithm allows finding models of phonemes, which provide a higher probability of correct recognition.</p>
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spelling doaj.art-c2289ded2fd7462199a11319ad955a6b2023-01-02T17:42:43ZengYaroslavl State UniversityМоделирование и анализ информационных систем1818-10152313-54172013-01-012022333197An Algorithm for Parameters Estimation of Autoregressive Model of Basic Speech UnitsI. V. Gubochkin0Нижегородский государственный лингвистический университет им. Н.А. Добролюбова<p>The article considers the problem of estimating autoregressive model parameters of elementary speech units such as phonemes. It is suggested an iterative algorithm based on the Newton numerical minimization technique to search an autoregressive model of phonemes specified its multiple samples. For this purpose the analytical expressions of the gradient and the Hessian of Kullback–Leibler information divergence between autoregressive models were computed. Experimental studies on a set of English phonemes showed that the developed algorithm requires less computational effort for large amounts of data, and iterations count depends little on the amount of input data as opposed to reference phoneme selection algorithm based on the criterion of a minimum sum of information divergence. Moreover, the proposed algorithm allows finding models of phonemes, which provide a higher probability of correct recognition.</p>http://mais-journal.ru/jour/article/view/203автоматическое распознавание речиэлементарные речевые единицыинформационное рассогласованиефонема
spellingShingle I. V. Gubochkin
An Algorithm for Parameters Estimation of Autoregressive Model of Basic Speech Units
Моделирование и анализ информационных систем
автоматическое распознавание речи
элементарные речевые единицы
информационное рассогласование
фонема
title An Algorithm for Parameters Estimation of Autoregressive Model of Basic Speech Units
title_full An Algorithm for Parameters Estimation of Autoregressive Model of Basic Speech Units
title_fullStr An Algorithm for Parameters Estimation of Autoregressive Model of Basic Speech Units
title_full_unstemmed An Algorithm for Parameters Estimation of Autoregressive Model of Basic Speech Units
title_short An Algorithm for Parameters Estimation of Autoregressive Model of Basic Speech Units
title_sort algorithm for parameters estimation of autoregressive model of basic speech units
topic автоматическое распознавание речи
элементарные речевые единицы
информационное рассогласование
фонема
url http://mais-journal.ru/jour/article/view/203
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AT ivgubochkin algorithmforparametersestimationofautoregressivemodelofbasicspeechunits