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文本文件的语音识别中汉语音节的特征分析^1
引用本文:张晓东,吴捷.文本文件的语音识别中汉语音节的特征分析^1[J].巢湖学院学报,2004,6(3):76-83.
作者姓名:张晓东  吴捷
作者单位:皖西学院物理与电子信息工程系,皖西学院物理与电子信息工程系 安徽 六安 237012,安徽 六安 237012
基金项目:安徽省教育厅自然科学基金(项目编号:03kj324)
摘    要:汉语语音识别中连续大词汇量的语音识别率较差。若能把连续大词汇量的语音进行实时自动切分为单个音节,便可提高系统的识别率。如何做到对语音识别中音节的自动切分,首先需找出汉语语音音节的特征。本文综合了当前对汉语音节特征的研究成果,通过深入地比较分析,系统地给出了汉语语音音节的功率谱特征和时域特征,为汉语语音音节的自动切分提供算法依据,对提高连续大词汇量语音的识别率有重要意义。

关 键 词:文本文件  语音识别  汉语音节  特征分析  短时能量  短时平均过零率  自相关函数  功率谱

The Features Analysis of Chinese Speech Syllable in Text-file Speech Recognition
ZHANG Xiao- dong.The Features Analysis of Chinese Speech Syllable in Text-file Speech Recognition[J].Chaohu College Journal,2004,6(3):76-83.
Authors:ZHANG Xiao- dong
Abstract:In Chinese speech recognition, the speech recognition ratio of continuous large vocabulary is comparatively poor. If speech sound of continuous large vocabulary can be automatically divided into segments of single syllable, the recognition ratio of system can be raised. How to make automatic segmentation of syllable in the recognition of speech sound? First of all, We should find out the features of Chinese speech syllable. Synthesizing current research achevements of Chinese syllabic feature with thorough comparison and analysis, this paper systematically offers the features of power spectrum and time-domain of Chinese speech syllable, Which provides algorithm basis for automatic segmentarion of Chinese speech syllable and has a great significance for raising the recognition ratio of continuous large vocabulary.
Keywords:Short Time average energy  Short Time average cross zero ratio  Autocorrelation function  Power spectrum
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