计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
7期
123-125
,共3页
高斯混合模型%病态嗓音%计盒维数%截距
高斯混閤模型%病態嗓音%計盒維數%截距
고사혼합모형%병태상음%계합유수%절거
Gaussian Mixture Model(GMM)%pathological voice%box-counting dimension%intercept
通过分析嗓音的发音机理,提取正常与病态嗓音的传统声学参数:基频、共振峰、Mel 倒谱系数(MFCC),以及非线性特征参数:计盒维数与截距,作为病态嗓音识别的特征矢量集.应用高斯混合模型(GMM)对156例正常嗓音与146例病态嗓音进行建模与识别.结果表明:非线性特征参数计盒维数与截距能很好地区分正常与病态嗓音,它们与传统声学参数基频和共振峰的组合,能够取得92.60%的识别率.
通過分析嗓音的髮音機理,提取正常與病態嗓音的傳統聲學參數:基頻、共振峰、Mel 倒譜繫數(MFCC),以及非線性特徵參數:計盒維數與截距,作為病態嗓音識彆的特徵矢量集.應用高斯混閤模型(GMM)對156例正常嗓音與146例病態嗓音進行建模與識彆.結果錶明:非線性特徵參數計盒維數與截距能很好地區分正常與病態嗓音,它們與傳統聲學參數基頻和共振峰的組閤,能夠取得92.60%的識彆率.
통과분석상음적발음궤리,제취정상여병태상음적전통성학삼수:기빈、공진봉、Mel 도보계수(MFCC),이급비선성특정삼수:계합유수여절거,작위병태상음식별적특정시량집.응용고사혼합모형(GMM)대156례정상상음여146례병태상음진행건모여식별.결과표명:비선성특정삼수계합유수여절거능흔호지구분정상여병태상음,타문여전통성학삼수기빈화공진봉적조합,능구취득92.60%적식별솔.
@@@@By analyzing the mechanism of pronunciation, normal and pathological voice of traditional acoustic parameters:fun-damental frequency, formant, Mel Frequency Cepstrum Coefficient(MFCC), and non-linear feature parameters:box-counting dimension and intercept, are extracted as feature vectors of recognition of pathological voice. 156 normal voice samples and 146 pathological voice samples are recognized based on Gaussian Mixture Model(GMM). The results show that the nonlinear fea-ture parameters of box-counting dimension and intercept can well distinguish between normal and pathological voice. The com-bination of box-counting dimension, intercept and the traditional acoustic parameters-fundamental frequency and formant can achieve a better recognition rate of 92.60%.
