On the KL Discreminant Information as Distance Measure for Speech Recognition

Zhan Puming; Lu Dajin & Wang Zuoying

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On the KL Discreminant Information as Distance Measure for Speech Recognition

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- On the KL Discreminant Information as Distance Measure for Speech Recognition
- Acta Electronica Sinica Issue 1, Pages: 1-7(1993)
- 作者机构：
  
  1. 清华大学电子工程系
  2. 清华大学电子工程系北京 100084
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：1993
- 稿件说明：
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[1]战普明,陆大,王作英.用KL鉴别信息作为语音识别距离测度的研究[J].电子学报,1993(01):1-7.

Zhan Puming, Lu Dajin & Wang Zuoying. On the KL Discreminant Information as Distance Measure for Speech Recognition[J]. Acta Electronica Sinica, 1993, (1): 1-7.
[1]战普明,陆大,王作英.用KL鉴别信息作为语音识别距离测度的研究[J].电子学报,1993(01):1-7. DOI：

Zhan Puming, Lu Dajin & Wang Zuoying. On the KL Discreminant Information as Distance Measure for Speech Recognition[J]. Acta Electronica Sinica, 1993, (1): 1-7. DOI：

摘要

本文采用KL（Kullback-Leibler）鉴别信息作为修正的隐Markov模型的距离测度。推导了该距离测度的解析式

证明了其收敛性。在不同大小的训练集和模型状态数下实验研究了模型距离

实验验证了这种距离测度用于语音识别的合理性和可行性。

Abstract

In this paper

the KL （Kullback-Leibler） discreminant information is used as a distance measure of the modified Hidden Markov Model. The analytic formulation of the distance measure is derived

and the convergence is proved. The model distance is given at different size of the training set and different numbers of the states

and the reasonablness of the distance for speech recognition is also shown in the experiments.

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