基于Fast ICA的多说话人识别系统

针对多人混合语音条件下说话人身份难以识别的问题,提出了一种使用快速独立分量分析（Fast ICA）方法分离各个说话人的语音信号,并采用RBF神经网络方法进行说话人识别的策略.由于不同语音源信号保持相对独立,利用盲信号分离的思想,使用Fast ICA方法用于信号的分离,从而对获得的独立语音数据分别提取说话人特征,采用RBF神经网络模型实现多说话人身份的识别.实验结果表明,该方法能有效地实现混合语音条件下的说话人识别.

Multi-speaker Recognition System Based on Fast ICA

It is difficult to recognize speakers when the sample voices are mixed.The paper proposes fast independent component analysis（Fast ICA） to separate individual speaker＇s voice signal from the mixed voice.Besides,a model of RBF neural network is applied to recognize the speaker.Different voice signals maintain relatively independent.Fast ICA method can be used for signal separation based on the idea of blind signal separation.As a result,features of individual speaker can be extracted from independent voice data.The model of RBF neural network achieves recognition of speakers.Experiments show that the system is able to identify individual speaker from mixed voice data.