Long span prosodic features for speaker recognition

In this paper, we first give an introduction about speaker recognition techniques. Then a novel speaker verification method based on long span prosodic features is proposed. After speech is pre-processed by a voice activity detection module, and basic prosody features are extracted for each speech unit, we carried out an approximation of the pitch, formant, time domain energy and harmonic energy contours by taking the leading terms in a Legendre polynomial expansion. HLDA is used to reduce the feature dimension and mean supervector in each individual Gaussian is used to represent the distribution of the time-frequency features. Experiments on NIST06 show that the proposed method can reduce the EER from 4.9% to 4.6% when fusing with the traditional MFCC-featured system.