Tiny Noise-Robust Voice Activity Detector for Voice Assistants

TL;DR

This paper introduces a lightweight noise-robust voice activity detector designed for on-device voice assistants, significantly improving accuracy in noisy environments without increasing model size or requiring fine-tuning.

Contribution

The paper presents a novel, lightweight VAD with added pre- and post-processing modules that enhance noise robustness without enlarging the model or needing fine-tuning.

Findings

Achieves higher accuracy in noisy environments compared to baseline models.

Maintains performance in clean speech detection.

Does not increase model complexity or require additional training.

Abstract

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.