关键词: 小波能量谱, 动态加权局部线性嵌入, 病理嗓音, 语音识别

Abstract:  A wavelet domain modeling method is proposed to analyze and recognize pathological voice. Multi-scale continuous wavelet transform is operated to analyze pathological voice in time-frequency domain, then the Gaussian mixture model of the energy spectrum is calculated along the scale axis direction, and maximum likelihood estimation method is employed to solve the model statistical parameters as the feature parameters. It uses the improved dynamic weighted locally linear embedding method to reduce the dimensionality of the feature parameters nonlinearly. The experimental results show that, the pathological voice recognition accuracy rate of the wavelet energy spectrum features after nonlinear dimensionality reduction recognition reaches 97.45%, and the dimensionality reduction effect of dynamic weighted locally linear embedding method is better than principal component analysis and locally linear embedding reduction method.

Key words: wavelet energy spectrum, dynamic weighted locally linear embedding, pathological voice, voice recognition