Acoustic segment modeling for speaker recognition

We propose a speaker recognition system based on the acoustic segment modeling technique. It is assumed that the overall sound characteristics for speakers can be covered by a set of acoustic segment models (ASMs) while the ASMs are acoustically-motivated self-organized sound units without imposing any phonetic definitions. These acoustic segment models decode a spoken utterance into a string of segment units and the mean vectors of ASMs based on the unsupervised MAP adaptation are concatenated to represent the characteristics of the specific speaker. Support vector machines are thus applied on these high dimensional feature vectors for speaker recognition. We evaluate the proposed approach in the 2006 NIST speaker recognition evaluation core condition test trials.