摘要
A speaker model called complete feature corpus (CFC) and an evaluation algorithm of mutual information (MIE) are proposed for text-independent speaker identification. The CFC model represents the speech and pronunciation characteristics of speaker by a feature vector corpus which was trained from some typical speech samples. It hires multi-step mini-max search matching scheme for MIE algorithm to evaluate the similarity of speech features between input speech and the models in distance and information space. Maximum mutual information (MMI) decision criterion is used to decide the identity of speaker. Experiments on performance analysis with comparison to GMM method show that proposed model and evaluation algorithm are quite effective and presented a higher performance than ordinary GMM method.
A speaker model called complete feature corpus (CFC) and an evaluation algorithm of mutual information (MIE) are proposed for text-independent speaker identification. The CFC model represents the speech and pronunciation characteristics of speaker by a feature vector corpus which was trained from some typical speech samples. It hires multi-step mini-max search matching scheme for MIE algorithm to evaluate the similarity of speech features between input speech and the models in distance and information space. Maximum mutual information (MMI) decision criterion is used to decide the identity of speaker. Experiments on performance analysis with comparison to GMM method show that proposed model and evaluation algorithm are quite effective and presented a higher performance than ordinary GMM method.
基金
The work is supported by the University Natural Science Fund (04KJA510133) of Jiangsu Province.
