Injecting Spatial Information for Monaural Speech Enhancement via Knowledge Distillation

TL;DR

This paper introduces a novel method to enhance monaural speech enhancement by injecting spatial information through knowledge distillation from binaural models, significantly improving performance in low SNR conditions.

Contribution

The paper proposes a new approach to incorporate spatial information into monaural speech enhancement using knowledge distillation from binaural models, enabling better speech reconstruction.

Findings

Achieves higher speech intelligibility and quality in low SNR conditions.

Outperforms other monaural SE models with fewer parameters.

Effectively learns binaural features through knowledge distillation.

Abstract

Monaural speech enhancement (SE) provides a versatile and cost-effective approach to SE tasks by utilizing recordings from a single microphone. However, the monaural SE lags performance behind multi-channel SE as the monaural SE methods are unable to extract spatial information from one-channel recordings, which greatly limits their application scenarios. To address this issue, we inject spatial information into the monaural SE model and propose a knowledge distillation strategy to enable the monaural SE model to learn binaural speech features from the binaural SE model, which makes monaural SE model possible to reconstruct higher intelligibility and quality enhanced speeches under low signal-to-noise ratio (SNR) conditions. Extensive experiments show that our proposed monaural SE model by injecting spatial information via knowledge distillation achieves favorable performance against other monaural SE models with fewer parameters.