Room response embeddings improve speech clarity in noisy environments
Room Impulse Response Embeddings for Speech Enhancement in Noisy and Reverberant Environments
Sound
Summary
Making speech clearer when there is background noise and echoes is tough. The authors developed a way for a computer to learn the unique sound patterns caused by room echoes from noisy recordings without needing labeled examples. They then use these learned patterns to help clean up the speech, making it easier to understand. Their method consistently improves how well speech sounds and how accurately it is transcribed even when noise and echoes are present.
What this means in practice
- •For audio device manufacturers: Enhance voice clarity in smart speakers and hearing aids by incorporating learned room acoustics embeddings to reduce noise and echo effects.$Commercial implications: Allows selling devices with improved speech recognition and audio quality in challenging acoustic settings using learned room response features.
- •For conference call software developers: Improve real-time speech enhancement in video calls by conditioning noise and echo reduction on embeddings that represent room acoustics.
Authors
Adrian Meise, Reinhold Haeb-Umbach
Abstract
We propose a self-supervised approach for learning room impulse response (RIR) representations from single-channel noisy-reverberant speech. It consists of first training on reverberant data, then on noisy-reverberant data, and finally with a teacher-student approach, where the student learns to replicate the teacher's embeddings when given a noisy version of the reverberant input. We assess their representational capabilities by estimating acoustic room parameters from them. Conditioning a discriminative speech enhancement model on the derived embeddings yields consistent gains across all evaluated metrics, including downstream word error rate, for both reverberant and noisy-reverberant speech.