Employing low-pass filtered temporal speech features for the training of ideal ratio mask in speech enhancement

no code implementations • ROCLING 2021 • Yan-Tong Chen, Zi-Qiang Lin, Jeih-weih Hung

Preliminary experiments conducted on a subset of TIMIT corpus reveal that the proposed method can make the resulting IRM achieve higher speech quality and intelligibility for the babble noise-corrupted signals compared with the original IRM, indicating that the lowpass filtered temporal feature sequence can learn a superior IRM network for speech enhancement.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2