Evaluating Deep Neural Network-based Speaker Verification Systems on Sinhala and Tamil Datasets

Abstract

Speaker verification, a biometric identifier, determines whether an input speech belongs to the claimed identity. The existing models for speaker verification have reported performances mainly in English, and no study has experimented with Sinhala and Tamil datasets. This study proposes a semi-automated pipeline to curate datasets for Sinhala and Tamil from videos on YouTube filmed under noisy and unconstrained conditions which represent real-world scenarios. Both Sinhala and Tamil datasets include utterances for 140 persons of interest (POIs) with more than 300 utterances per POI under one or more genres: interviews, speeches, and vlogs. Moreover, this study investigates how domain mismatch affects a speaker verification model trained in English and applied to Sinhala and Tamil. Two deep neural network models trained in English show significant performance drops on Sinhala and Tamil datasets compared to an English dataset as expected due to domain mismatch, however, it is observed that AM-softmax performed better than vanilla softmax. In the future, robust speaker verification models with domain adaptation techniques will be built to improve performance on Sinhala and Tamil datasets.