An acoustic segment model approach to incorporating temporal information into speaker modeling for text-independent speaker recognition

We propose an acoustic segment model (ASM) approach to incorporating temporal information into speaker modeling in text-independent speaker recognition. In training, the proposed framework first estimates a collection of ASM-based universal background models (UBMs). Multiple sets of speaker-specific ASMs are then obtained by adapting the ASM-based UBMs with speaker-specific enrollment data. A novel usage of language models of the ASM units is also proposed to characterize transitions among ASMs. In the testing phase the ASM sets for the claimed speaker and UBMs, along with a bigram ASM language model, are used to calculate detection scores for each given test utterance. We report on speaker recognition experiments using the NIST 2001 SRE database. The results clearly indicate that the proposed ASM-based method achieves a notable improvement over the GMM-based speaker modeling in which no temporal modeling is considered. Moreover, a further error reduction is obtained by integrating the language model, another inclusion of temporal properties made possibly by ASM based speaker modeling.