A Latent Dirichlet Allocation Based Front-End for Speaker Verification

Latent Dirichlet Allocation is a powerful topic model used heavily in natural language processing, image processing and biomedical signal processing fields to discover hidden structures behind observed data. In this work, we have adopted a variant of LDA for continuous descriptor vectors and use this model as a front-end for speaker verification similar to popular i-vector front-end. We have proposed an efficient hierarchical acoustic vocabulary creation method and presented a speaker verification system using latent topic probability features obtained using LDA front-end. We analysed the performance of the LDA front-end for various vocabulary and topic sizes, and obtained encouraging results on NIST SRE corpora. The proposed system is shown to improve the performance of an i-vector-PLDA baseline system when tested on NIST SRE12 corpora.