轻量级语音活动检测模型，有效提升嘈杂环境下的语音识别准确率。
Tiny Noise-Robust Voice Activity Detector for Voice Assistants
- 通过预处理和后处理模块增强噪声鲁棒性，无需增大模型或微调。
- 在高噪声环境下性能显著优于基线模型，同时提升纯净语音检测精度。
- 适合部署于手机、耳机等资源受限的AIoT设备，实用性强。
在背景噪声环境下，语音活动检测（VAD）仍是语音处理中的难题。准确的VAD对自动语音识别、语音转文字、对话代理等应用至关重要，而噪声会严重降低性能。现代应用场景包括部署在人工智能物联网（AIoT）设备（如手机、智能眼镜、耳机）上的语音助手，其语音信号常伴随背景噪声。因此，VAD模块必须保持轻量化以适应设备端限制。现有模型在不同声学环境下的低信噪比条件下表现不佳，简单VAD在干净环境中有效，但在嘈杂场景下难以检测语音。本文提出一种噪声鲁棒的轻量级VAD，通过引入数据预处理和后处理模块来应对背景噪声。该方法显著提升了噪声环境中的检测准确率，且无需更大模型或微调。实验表明，本方法相比基线模型有明显改进，尤其在高噪声干扰环境下。此外，该改进还提升了纯净语音的检测效果。
原文摘要 · Abstract (English)
Voice Activity Detection (VAD) in the presence of background noise remains a challenging problem in speech processing. Accurate VAD is essential in automatic speech recognition, voice-to-text, conversational agents, etc, where noise can severely degrade the performance. A modern application includes the voice assistant, specially mounted on Artificial Intelligence of Things (AIoT) devices such as cell phones, smart glasses, earbuds, etc, where the voice signal includes background noise. Therefore, VAD modules must remain light-weight due to their practical on-device limitation. The existing models often struggle with low signal-to-noise ratios across diverse acoustic environments. A simple VAD often detects human voice in a clean environment, but struggles to detect the human voice in noisy conditions. We propose a noise-robust VAD that comprises a light-weight VAD, with data pre-processing and post-processing added modules to handle the background noise. This approach significantly enhances the VAD accuracy in noisy environments and requires neither a larger model, nor fine-tuning. Experimental results demonstrate that our approach achieves a notable improvement compared to baselines, particularly in environments with high background noise interference. This modified VAD additionally improving clean speech detection.
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