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arXiv · 2406.04791

Speaker-Smoothed kNN Speaker Adaptation for End-to-End ASR

Shaojun Li·Daimeng Wei·Hengchao Shang·Jiaxin Guo·ZongYao Li·Zhanglin Wu·Zhiqiang Rao·Yuanchang Luo·Xianghui He·Hao Yang
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Abstract

Despite recent improvements in End-to-End Automatic Speech Recognition (E2E ASR) systems, the performance can degrade due to vocal characteristic mismatches between training and testing data, particularly with limited target speaker adaptation data. We propose a novel speaker adaptation approach Speaker-Smoothed kNN that leverages k-Nearest Neighbors (kNN) retrieval techniques to improve model output by finding correctly pronounced tokens from its pre-built datastore during the decoding phase. Moreover, we utilize x-vector to dynamically adjust kNN interpolation parameters for data sparsity issue. This approach was validated using KeSpeech and MagicData corpora under in-domain and all-domain settings. Our method consistently performs comparably to fine-tuning without the associated performance degradation during speaker changes. Furthermore, in the all-domain setting, our method achieves state-of-the-art results, reducing the CER in both single speaker and multi-speaker test scenarios.

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Shaojun Li, Daimeng Wei, Hengchao Shang, Jiaxin Guo, ZongYao Li, Zhanglin Wu, Zhiqiang Rao, Yuanchang Luo, Xianghui He, Hao Yang. 2024-06-07. Speaker-Smoothed kNN Speaker Adaptation for End-to-End ASR. https://arxiv.org/abs/2406.04791

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