Accelerating Speech Recognition Algorithm with Synergic Hidden Markov Model and Genetic Algorithm based on Cellular Automata

被引：1

作者：

Mosleh, Mohammad ^{[1
]}

Setayeshi, Saeed ^{[2
]}

Kheyrandish, Mohammad ^{[1
]}

机构：

[1] Islamic Azad Univ, Dep Comp Eng, Dezful, Iran

[2] Amirkabir Univ, Dept Nuclear Eng, Tehran, Iran

来源：

PROCEEDINGS OF THE 2009 INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING SYSTEMS | 2009年

关键词：

Speech recognition; Hidden Markov Model (HMM); Genetic Algorithm (GA); Cellular Automata (CA);

D O I：

10.1109/ICSPS.2009.37

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

One of the best current methods for modeling dynamic speech signal is using of HAM model. The speech recognition systems based on HMM can be able to compute the best likelihood measure between unknown input pattern and reference models by using Viterbi algorithm. Whereas such algorithm is based on dynamic programming, it consists of many computations with increasing number of reference words. In this paper, we will present a new evolutionary methodology based on synergic HMM and GA that will be able to compute likelihood measurement between unknown input pattern and reference patterns in the parallel form and based on cellular automata. We introduce this algorithm as HGC. The HGC algorithm will be compared with the Viterbi algorithm from the "recognition accuracy" and "recognition speed" viewpoints. Obtained results show that the HGC and Viterbi algorithms are close from "recognition accuracy" viewpoint, but HGC isso faster than the Viterbi

引用

页码：3 / +

页数：2

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