A Score-Level Solution to Speaker Verification Using UBM Pooling and Adaptive Cohort Selection

摘要

In an environment that is highly unpredictable in nature, a speaker verification system needs a good background model to carry out the verification task reliably. In this paper, a 1024-component UBM is created by pooling a noisy speech UBM and clean speech UBM. This pooled UBM is used for speaker adaptation as well as for speaker testing. Experimental results have shown minor improvement with pooled UBM as compared to baseline UBM. In addition to this, a score-level solution is proposed by means of cohort model selection using HT-normalization to reduce undesirable variation arising from acoustically mismatched devices and environment. For cohort selection a simple distance metric based on similarity modeling of each client speaker is used. The normalization parameters computed over a group of speakers (cohort) having some common characteristics are used in the final score calculation. Experiments on a noisy corpus has shown reasonable improvements in performance, when normalization parameters were taken from a cohort than from a general group. Experiments have shown a recognition rate of 90.58 and 87.64% for matched handset type in office and roadside environment respectively.