A discriminative training approach for text-independent speaker recognition

Gaussian mixture model (GMM) has been commonly used for text-independent speaker recognition. The estimation of model parameters is generally performed based on the maximum likelihood (ML) criterion. However, this criterion only utilizes the labeled utterances for each speaker model and very likely leads to a local optimization solution. To solve this problem, this paper proposes a discriminative training approach based on the maximum model distance (MMD) criterion. We investigate the characteristics of speaker recognition and further propose a novel selection strategy of competing speakers associated with it. Experimental results based on the KING and TIMIT databases demonstrate that our training approach was quite efficient to improve the performance of speaker identification and verification. When there were three training sentences for each speaker, the verification equal error rate (EER) of 168 speakers in TIMIT could be reduced by 30.4% compared with the conventional method.