Context-aware data augmentation for enhanced speech command recognition in industrial environments

Abstract

In Human-Robot Interaction, speech is one of the most intuitive and effective communication channel. In Industry 4.0, speech-based communication can significantly enhance productivity and efficiency on production lines. However, deploying a Speech Command Recognition Module in real-world industrial settings poses challenges, as the system must balance two conflicting objectives: accurately recognizing commands while rejecting noise and irrelevant speech. To address this, we propose a modular framework designed to optimize recognition accuracy and rejection robustness while minimizing the need for extensive industrial dataset collection. The framework features an efficient Command Recognition module trained on laboratory-collected data augmented with synthetic samples. Advanced context-aware data augmentation techniques and dynamic noise injection further enhance the model's robustness. To improve reliability in noisy environments, a Keyword Spotting module is introduced, activating the recognition system only when a predefined keyword is detected. The proposed system was evaluated using real-world samples collected in a noisy industrial setting. The results demonstrated a high recall rate for both command recognition and noise rejection, confirming the system's effectiveness in meeting the demands of industrial applications.