15th Annual Conference of the International Speech Communication Association

September 14-18, 2014

Speech Enhancement from Additive Noise and Channel Distortion — A Corpus-Based Approach

Ji Ming, Danny Crookes

Queen's University Belfast, UK

This paper presents a new approach to single-channel speech enhancement involving both noise and channel distortion (i.e., convolutional noise). The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise. Third, we present an iterative algorithm for improved speech estimates. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement.

Full Paper

Bibliographic reference.  Ming, Ji / Crookes, Danny (2014): "Speech enhancement from additive noise and channel distortion — a corpus-based approach", In INTERSPEECH-2014, 2710-2714.