Vector Quantization Mappings for Speaker Verification

Abstract

In speaker verification several techniques have emerged to map variable length utterances into a fixed dimensional space for classification. One popular approach uses Maximum A-Posteriori (MAP) adaptation of a Gaussian Mixture Model (GMM) to create a super-vector. This paper investigates using Vector Quantisation (VQ) as the global model to provide a similar mapping. This less computationally complex mapping gives comparable results to its GMM counterpart while also providing the ability for an efficient iterative update enabling media files to be scanned with a fixed length window.