根据语音分形维和基音周期的说话人性别识别研究

根据语音特征实现说话人性别的自动识别,在音频处理与分析中具有重要的应用意义.为了克服语音常规线性参数在刻画说话人性别特征上的不足,本文使用了分形维等非线性参数作为特征空间的有效补偿.首先利用提升算法实现基音周期的提取;然后提取语音的分形维数;最后根据Takens定理,对分形维进行了重构,采用求近似熵的方法得到分形维复杂度.将基音周期、分形维数以及分形维复杂度构成三维向量,进行说话人的性别识别.实验证明,通过非线性参数的介入,与仅使用基音周期等传统线性特征的识别方法相比,识别系统的准确率和稳定性得到有效提高,因此为说话人性别识别提供了一个新的思路.

Speaker Gender Identification Based on Audio Fractal Dimension and Pitch Feature

Automatic speaker gender identification based on voice feature is an important task in voice processing and analysis fields. In this paper non-linear parameters such as fractal dimension are applied to be one part of feature space for improving the ability of describing speaker gender feature through conventional linear parameters method. Pitch is picked using lifting scheme, and audio fractal dimension is extracted. Then based on Takens theory, the time delay method is used to reconstruct the phase space of fractal dimension sequence. And fractal dimension complexity is obtained by calculating Approximate Entropy. Three dimension feature vectors, including the pitch, the fractal dimension and the fractal dimension complexity, are applied to speaker gender identification. Experiment results show that through adding non-linear parameters, compared with the linear parameter using one dimension only such as pitch, the proposed method is more accurate and robust, and thus provides a new way for speaker gender identification.