Comparison of Biomimetic Pattern Recognition, HMM and DTW for Speaker-Independent Speech Recognition

QIN Hong; WANG Shou-jue

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Comparison of Biomimetic Pattern Recognition, HMM and DTW for Speaker-Independent Speech Recognition

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- Comparison of Biomimetic Pattern Recognition, HMM and DTW for Speaker-Independent Speech Recognition
- Acta Electronica Sinica Vol. 33, Issue 5, Pages: 957-960(2005)
- 作者机构：
  
  中国科学院半导体研究所神经网络实验室,北京,100083
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN912
- Published：2005
- 稿件说明：
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QIN Hong, WANG Shou-jue. Comparison of Biomimetic Pattern Recognition, HMM and DTW for Speaker-Independent Speech Recognition[J]. Acta Electronica Sinica, 2005, 33(5): 957-960.
DOI：

QIN Hong, WANG Shou-jue. Comparison of Biomimetic Pattern Recognition, HMM and DTW for Speaker-Independent Speech Recognition[J]. Acta Electronica Sinica, 2005, 33(5): 957-960. DOI：

摘要

本文将基于多权值神经元网络的仿生模式识别方法用于连续语音有限词汇量固定词组识别的研究中

并将其识别效果与HMM方法及DTW方法进行了比较分析.以15个词组的词汇表做测试

通过调整这三种识别算法的参数

在它们的拒识率相同的情况下

针对参加训练的词汇

比较他们的错误识别率(某类误认为他类);针对未参加训练的词汇

比较他们的错误接受率(误认为某类).结果表明

在低训练样本数量的情况下

仿生模式识别方法能获得更好的识别效果.

Abstract

The purpose of this paper is to compare the performance of three speech recognition methods

one based on Biomimetic Pattern Recognition (BPR) and the other two based on Hidden Markov Models (HMMs) and Dynamic Time Warping (DTW) respectively.As a general purpose model of pattern Recognition

BPR is realized by Multi-Weights Neuron Networks.For the 15 words vocabulary

we analyze the false recognition rate (ratio of accepting a trained word to another trained word) and false acceptance rate (ratio of accepting an untrained word to a trained word) respectively.Experiment results show that when the training data was not sufficient

the manner of BPR achieved a higher performance.

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