research-article

Wavoice: A Noise-resistant Multi-modal Speech Recognition System Fusing mmWave and Audio Signals

Authors:

Kui RenAuthors Info & Claims

SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems

Pages 97 - 110

https://doi.org/10.1145/3485730.3485945

Published: 15 November 2021 Publication History

Abstract

With the advance in automatic speech recognition, voice user interface has gained popularity recently. Since the COVID-19 pandemic, VUI is increasingly preferred in online communication due to its non-contact. Additionally, various ambient noise impedes the public applications of voice user interfaces due to the requirement of audio-only speech recognition methods for a high signal-to-noise ratio. In this paper, we present Wavoice, the first noise-resistant multi-modal speech recognition system that fuses two distinct voice sensing modalities, i.e., millimeter-wave (mmWave) signals and audio signals from a microphone, together. One key contribution is that we model the inherent correlation between mmWave and audio signals. Based on it, Wavoice facilitates the real-time noise-resistant voice activity detection and user targeting from multiple speakers. Furthermore, we elaborate on two novel modules into the neural attention mechanism for multi-modal signals fusion, and result in accurate speech recognition. Extensive experiments verify Wavoice's effectiveness under various conditions with the character recognition error rate below 1% in a range of 7 meters. Wavoice outperforms existing audio-only speech recognition methods with lower character error rate and word error rate. The evaluation in complex scenes validates the robustness of Wavoice.

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Index Terms

Wavoice: A Noise-resistant Multi-modal Speech Recognition System Fusing mmWave and Audio Signals
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems

November 2021

686 pages

ISBN:9781450390972

DOI:10.1145/3485730

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Published: 15 November 2021

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November 15 - 17, 2021

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SenSys '21 Paper Acceptance Rate 25 of 139 submissions, 18%;

Overall Acceptance Rate 174 of 867 submissions, 20%

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Wang CLin FYan HWu TXu WRen KBalzarotti DXu W(2024)VibSpeechProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699124(3997-4014)Online publication date: 14-Aug-2024
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