Random vector quantisation modelling in automatic speaker verification

Abstract

Automatic speaker verification (ASV) is a binary classification task. It consists of accepting or rejecting the claimed identity. ASV system has to decide whether a claimed speaker uttered a sentence. This paper proposes an algorithm called impostor random vector quantisation (IRVQ) based on multiples random codebook. IRVQ represents the impostor model also called universal background model (UBM) and we compare it to the second algorithm called partial impostor VQ (IVQ) for vector quantisation (VQ) in speaker verification. The present study demonstrates that the several random selected codebooks representing impostor models give better results and less half total error HTER than impostor IVQ method and baseline system. The performance of these models is evaluated on Arabic speaker verification dataset. However, this improvement also depends on the codebook size. The assumption concerning partitioning impostor acoustic spaces and choosing the best subspace to represent impostor model specific to each speaker is an efficient approach.