Modeling long-range dependencies in speech data for text-independent speaker recognition

In the paper, a new approach for modeling and matching long-range dependencies in free-text speech data is proposed for speaker recognition. The new approach consists of a sentence model to detail up to sentence-level dependencies in the training data, and a search algorithm that is capable of locating the matches of arbitrary-length segments between the training and testing sentences. The search algorithm is optimized to increase the probability for the match of long, continuous segments as opposed to short, separated segments, assuming that long, continuous segments contain more specific information about the speaker. The new approach has been evaluated on the NIST 1998 Speaker Recognition Evaluation database, and has shown improved performance.