Improving Speaker Recognition with Quality Indicators

Abstract

Nuisance factors such as short duration, noise and transmission conditions still pose accuracy challenges to state-of-the-art automatic speaker verification (ASV) systems. To address this problem, we propose a no reference system that consumes quality indicators encapsulating information about duration of speech, acoustic events and codec artifacts. These quality indicators are used as estimates to measure how close a given speech utterance would be to a high-quality speech segment uttered by the same speaker. The proposed measures when fused with a baseline ASV system are found to improve the performance of speaker recognition. The experimental study carried on a modified version of the NIST SRE 2019 dataset shows a relative decrease of 9.6% in equal error rate (EER) compared to the baseline.