辅助变量粒子滤波的声道共振特性轨迹跟踪  

Vocal tract resonances tracking by auxiliary vector particle filters

作　　者：王叶斌[1] 赵鹤鸣[1] 

机构地区：[1]苏州大学电子信息学院,苏州215021

出　　处：《声学学报》2009年第3期275-280,共6页Acta Acustica

基　　金：国家自然科学基金资助项目(60572076)

摘　　要：提出了一种基于辅助变量粒子滤波技术的连续语音声道共振特性(VTRs)轨迹跟踪方法。该方法基于描述语音信号特征的状态空间模型,采用粒子滤波技术跟踪VTRs的轨迹。语音模型由具有目标导向特性的动态方程和VTRs至倒谱系数(LPCC)的非线性映射构成。该方法有两个特点:首先,采用粒子滤波技术来处理语音模型的非线性问题;其次,在语音模型的状态方程中嵌入辅助变量用于标示VTRs在频域中的分布信息,并为粒子滤波过程中的粒子抽取提供目标导向。实验结果表明,该方法只需少量粒子即可正确跟踪连续语音的VTRs轨迹,而且可以在跟踪过程中避免虚假峰和合并峰的干扰。An auxiliary vector particle filtering method was presented for vocal tract resonances （VTRs） tracking. The proposed method uses particle filter based on a version of state-space model which describes the characteristics of speech signal. The speech model consists of a target-guided dynamic function and a non-linear prediction mapping from resonance frequencies and bandwidths to LPC cepstra （LPCC）. There are two characteristics in the proposed method. First, particle filtering technique is proposed to solve the non-linear problem of speech model. Second, an auxiliary vector, embedded in the state function of speech model, is used to incorporate the most current observations and to generate the proposal distribution of particle filter. Experimental results show that the proposed method is able to track the VTRs of fluent speech utterance efficiently with a small number of particles and able to solve the problem of spurious peaks and merging peaks.

关 键 词：粒子滤波 轨迹跟踪 共振特性 辅助变量 声道 连续语音 状态空间模型 语音模型 

分 类 号：O422.6[理学—声学]