Effective Acoustic Model-Based Beamforming Training for Static and Dynamic Hri Applications

Area of Science:

• Robotics
• Artificial Intelligence
• Acoustic Signal Processing

Background:

• Human-robot collaboration is vital for the fourth industrial revolution across diverse applications.
• Effective human-robot interaction (HRI) necessitates robots understanding human intentions via advanced user profiling.
• Voice communication is key for HRI, but challenging acoustic environments degrade speech signal quality.

Purpose of the Study:

• To implement a beamforming system for improved signal-to-noise ratio (SNR) and speech recognition on moving robotic platforms.
• To enhance human-robot interaction (HRI) in both static and dynamic contexts.
• To evaluate deep learning-based beamformers trained with measured room impulse responses (RIRs).

Main Methods:

• Development of a deep learning-based beamforming system for robotic platforms.
• Training using acoustic model-based multi-style training with measured room impulse responses (RIRs).
• Testing the system in static and dynamic environments, including robot motion.

Main Results:

• The deep learning beamformer significantly improved SNR and speech recognition accuracy.
• Training with measured RIRs outperformed training with simulated or matched RIRs, especially in dynamic conditions.
• The approach proved effective across various environments without needing extra data.

Conclusions:

• Deep learning-based beamforming substantially enhances HRI performance in challenging acoustic settings.
• Training with diverse measured RIRs is sufficient for robust HRI, simplifying data requirements.
• This technology is critical for advancing human-robot collaboration in Industry 4.0 applications.