赵利博,刘奇,付方玲,何凌.基于小波变换和倒谱分析的腭裂高鼻音等级自动识别[J].计算机科学,2018,45(4):278-284
基于小波变换和倒谱分析的腭裂高鼻音等级自动识别
Automatic Detection of Hypernasality Grades Based on Discrete Wavelet Transformation and Cepstrum Analysis
投稿时间:2017-03-06  修订日期:2017-06-17
DOI:10.11896/j.issn.1002-137X.2018.04.047
中文关键词:  腭裂,高鼻音,分类系统,小波分解系数倒谱
英文关键词:Cleft palate,Hypernasality,Recognition system,Wavelet decomposition coefficient cepstrum
基金项目:本文受国家自然基金青年科学基金项目(61503264)资助
作者单位E-mail
赵利博 四川大学电气信息学院 成都610065 ling.he@scu.edu.cn 
刘奇 四川大学电气信息学院 成都610065  
付方玲 四川大学电气信息学院 成都610065  
何凌 四川大学电气信息学院 成都610065  
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中文摘要:
      为实现对腭裂高鼻音等级的自动识别,通过对语音信号小波处理和特征提取方法的综合研究,提出基于小波分解系数倒谱特征的腭裂高鼻音等级自动识别算法。目前,研究人员对腭裂语音的研究多基于MFCC、Teager能量、香农能量等特征,识别正确率偏低,且计算量过大。文中对4种等级腭裂高鼻音的1789个元音\a\语音数据提取小波分解系数倒谱特征参数,使用KNN分类器对4种不同等级的高鼻音进行自动识别,将识别结果与MFCC、LPCC、基音周期、共振峰和短时能量共5种经典声学特征的识别结果作比较,同时使用SVM分类器对不同等级的腭裂高鼻音进行自动识别,并与KNN分类器进行对比。实验结果表明,基于小波分解系数倒谱特征的识别结果优于经典声学特征,且KNN分类器的识别结果优于SVM分类器。小波分解系数倒谱特征在KNN中的识别率最高达到91.67%,在SVM中达到87.60%,经典声学特征在KNN分类器中的识别率为21.69%~84.54%,在SVM中的识别率为30.61%~78.24%。
英文摘要:
      This paper proposed an automatic hypernasality grades classification algorithm in cleft palate speech based on discrete wavelet decomposition coefficients and cepstrum analysis.Currently,the widely used features to classify hypernasality grades include MFCC,Teager energy,Shannon energy and so on.However,the classification accuracy is low,and the computation amount is large.The speech data tested in this work include 1789 Mandarin syllables with the final \a\,which are spoken by cleft palate patients with four grades of hypernasality.The wavelet decomposition coefficientcepstrum was extracted as the acoustic feature,and then KNN classifier was applied to identify four grades of hyperna-sality.The classification performance was compared with five acoustic features:MFCC,LPCC,pitch period,formant and short-time energy.Meanwhile,the performance of KNN was compared with SVM classifier.The experimental results indicate that the recognition accuracy obtained by using wavelet decomposition coefficient cepstrum feature is higher than that obtained by using five classical acoustics features.The classification accuracy is higher when using KNN than SVM classifier.Recognition accuracy obtained by using wavelet decomposition coefficient cepstrum feature combined with KNN is 91.67%,and 87.60% combined with SVM.Recognition accuracy using classical acoustics features combined with KNN is only 21.69%~84.54%,and 30.61%~78.24% combined with SVM.
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