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arXiv · 2210.02904

WakeUpNet: A Mobile-Transformer based Framework for End-to-End Streaming Voice Trigger

Zixing Zhang·Thorin Farnsworth·Senling Lin·Salah Karout
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Abstract

End-to-end models have gradually become the main technical stream for voice trigger, aiming to achieve an utmost prediction accuracy but with a small footprint. In present paper, we propose an end-to-end voice trigger framework, namely WakeupNet, which is basically structured on a Transformer encoder. The purpose of this framework is to explore the context-capturing capability of Transformer, as sequential information is vital for wakeup-word detection. However, the conventional Transformer encoder is too large to fit our task. To address this issue, we introduce different model compression approaches to shrink the vanilla one into a tiny one, called mobile-Transformer. To evaluate the performance of mobile-Transformer, we conduct extensive experiments on a large public-available dataset HiMia. The obtained results indicate that introduced mobile-Transformer significantly outperforms other frequently used models for voice trigger in both clean and noisy scenarios.

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Zixing Zhang, Thorin Farnsworth, Senling Lin, Salah Karout. 2022-10-06. WakeUpNet: A Mobile-Transformer based Framework for End-to-End Streaming Voice Trigger. https://arxiv.org/abs/2210.02904

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