Teacher-Student Training for Text-Independent Speaker Recognition

By Raymond W. M. Ng, Xuechen Liu, Pawel Swietojanski. 2018

15 citations recorded by the source corpus.

This paper investigates text-independent speaker recognition using neural embedding extractors based on the time-delay neural network. Our primary focus is to explore the teacher-student (TS) training framework for knowledge distillation in a text-independent (TI) speaker recognition task. We report the results on both proprietary and public benchmarks, obtaining competitive results with 88–93% smaller models. Particularly, in clean testing conditions, we find TS training on neural-based TI systems achieved same or better performance than the i-vector based counterparts. Neural embeddings are less prone to short segment issues, and offer better performance particularly in the high-recall setting. They can also provide some additional insights about speakers, such as gender or how difficult a given speaker can be for recognition.

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1. Jianping Gou
2. Dacheng Tao
3. Baosheng Yu
4. S. Maybank
5. Changxing Ding
6. Chao-qun Hong
7. Jing Zhang
8. Jun Yu

Citations in the corpus

• Knowledge Distillation: A Survey (2020, background): “…identification (Shen et al. 2018, 2019c, 2020), audio classification (Gao et al. 2019; Perez et al. 2020), text-independent speaker recognition (Ng et al. 2018), speech enhancement (Watanabe et al. 2017), acoustic event detection (Price et al. 2016; Shi et al. 2019a,b), speech synthesis (Oord…”