Exploiting long-range temporal dynamics of speech for noise-robust speaker recognition

Temporal dynamics is an important feature of speech that distinguishes speech from noise, as well as distinguishing between different speakers. In this paper, we present an approach to maximally extract this feature of speech to improve the robustness against background noise, for text-independent speaker recognition. The new approach identifies and compares the longest matching speech segments between the training and test speech to increase noise immunity. Experiments have been conducted on the NIST 2002 SRE database in the presence of various types of noise including fast-varying song and music. The new approach has shown significantly improved performance over conventional noise-robust techniques.