Lend me an Ear: Speech Enhancement Using a Robotic Arm with a Microphone Array

2026-02-19

AI Summary

This work proposes an adaptive speech enhancement system that integrates robotic motion control with audio signal processing to address the limitations of conventional methods in high-noise industrial environments. The system employs a seven-degree-of-freedom robotic arm equipped with a 16-microphone array, dynamically reconfiguring the array geometry to optimize speech capture from a target speaker. By synergistically combining sound source localization, computer vision, inverse kinematics, minimum variance distortionless response (MVDR) beamforming, and a deep time-frequency masking network, the approach achieves superior speech acquisition performance. Notably, it introduces— for the first time—a physically reconfigurable microphone array mechanism, significantly outperforming fixed-array configurations across various signal-to-noise ratios. Experimental results demonstrate consistent improvements in scale-invariant signal-to-interference ratio and substantial reductions in word error rate.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Speech
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Speech enhancement performance degrades significantly in noisy environments, limiting the deployment of speech-controlled technologies in industrial settings, such as manufacturing plants. Existing speech enhancement solutions primarly rely on advanced digital signal processing techniques, deep learning methods, or complex software optimization techniques. This paper introduces a novel enhancement strategy that incorporates a physical optimization stage by dynamically modifying the geometry of a microphone array to adapt to changing acoustic conditions. A sixteen-microphone array is mounted on a robotic arm manipulator with seven degrees of freedom, with microphones divided into four groups of four, including one group positioned near the end-effector. The system reconfigures the array by adjusting the manipulator joint angles to place the end-effector microphones closer to the target speaker, thereby improving the reference signal quality. This proposed method integrates sound source localization techniques, computer vision, inverse kinematics, minimum variance distortionless response beamformer and time-frequency masking using a deep neural network. Experimental results demonstrate that this approach outperforms other traditional recording configruations, achieving higher scale-invariant signal-to-distortion ratio and lower word error rate accross multiple input signal-to-noise ratio conditions.

Problem

• speech enhancement
• noisy environments
• industrial settings
• microphone array
• acoustic conditions

Innovation

• robotic microphone array
• physical reconfiguration
• speech enhancement
• adaptive beamforming
• deep neural network