BSML: Bidirectional Sampling Aggregation-based Metric Learning for Low-resource Uyghur Few-shot Speaker Verification

Abstract

In recent years, text-independent speaker verification has remained a hot research topic, especially for the limited enrollment and/or test data. At the same time, due to the lack of sufficient training data, the study of low-resource few-shot speaker verification makes the models prone to overfitting and low accuracy of recognition. Therefore, a bidirectional sampling aggregation-based meta-metric learning method is proposed to solve the low-accuracy problem of speaker recognition in a low-resource environment with limited data, termed bidirectional sampling multi-scale Fisher feature fusion (BSML). First, the BSML method was used for effective feature enhancement in the feature extraction stage; second, a large number of similar and disjoint tasks were used to train the models to learn how to compare sample similarity; finally, new tasks were used to identify unknown samples by calculating the similarity of the samples. Extensive experiments are conducted on a short-duration text-independent speaker verification dataset generated from the THUYG-20 low-resource Uyghur with limited data, which comprised speech samples of diverse lengths. The experimental result has shown that the metric learning approach is effective in avoiding model overfitting and improving model generalization, with significant results in the identification of short-duration speaker verification in low-resource Uyghur with few-shot. It also demonstrates that BSML outperforms the state-of-the-art deep-embedding speaker recognition architectures and recent metric learning approach by at least 18%–67% in the few-shot test set. The ablation experiments further illustrate that our proposed approaches can achieve substantial improvement over prior methods and achieves better performance and generalization ability.