摘要
为了对腭裂语音的高鼻音进行等级区分,提出基于声学特征参数分析的腭裂语音高鼻音等级自动识别算法,提取基于香农能量和Mel倒谱系数(Mel frequency cepstrum coefficient,MFCC)的S-MFCC作为声学特征参数,结合高斯混合模型(Gaussian mixture model,GMM)分类器实现对腭裂语音4类高鼻音等级(正常、轻度、中度和重度)的自动识别。实验结果表明,提出的自动识别算法取得了较高的高鼻音类别正确识别率,对4类高鼻音的平均识别率达到79%以上,其中,提出的S-MFCC参数取得了85%的平均正确识别率,优于传统的香农能量算法、MFCC算法,具有较高的临床应用价值。
In order to detect hypemasal automatically for cleft palate patients, based on Shannon energy and Me1 frequency cepstrum co- efficient acoustic features and by combining with Gaussian mixture model classifier, an automatic hypernasal detection algorithm was proposed. The experiment results showed that the presented method achieved a good performance on the detection of four levels of hy- pemasal, such as normal, low-level, moderate-level and high-level. The average classification accuracies for four levels of hypemasal were over 79%. Moreover, the correct recognition accuracy using energy plus Mel frequency cepstrum coefficient feature set reached up to 85%. The classification of hypernasal levels has important clinical applications.
出处
《四川大学学报（工程科学版）》
EI
CAS
CSCD
北大核心
2014年第2期127-132,共6页
Journal of Sichuan University (Engineering Science Edition)
基金
国家自然科学基金青年基金资助项目(30900391)
关键词
腭裂语音
高鼻音
香农能量
MEL倒谱系数
高斯混合模型识别器
cleft palate speech
hypernasality
Shannon energy
Mel frequency cepstrum coefficient
Gaussian mixture model classifier
