Year: 2025
Type: article
Abstract: Human-robot interaction (HRI) via voice command has significantly advanced in recent years, with large Vision-Language-Action (VLA) models demonstrating particular promise in human-robot voice interaction. However, these systems still struggle with environmental noise contamination during voice interaction and lack a specialized denoising network for multi-speaker command isolation in an overlapping speech scenario. To overcome these challenges, we introduce a method to enhance voice command-based HRI in noisy environments, leveraging synthetic data and a self-supervised denoising network to enhance its real-world applicability. Our approach focuses on improving self-supervised network performance in denoising mixed-noise audio through training data scaling. Extensive experiments show our method outperforms existing approaches in simulation and achieves 7.5% higher accuracy than the state-of-the-art method in noisy real-world environments, enhancing voice-guided robot control.
Source: Biomimetics
Authors: Mu Li, Wenjin Xu, Chao Zeng, Ning Wang
Language: English
Topic: Speech and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,654
OpenAlex ID: W4414098596