An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

Ming Ji, Danny Crookes

Research output: Contribution to journalArticle

8 Citations (Scopus)
222 Downloads (Pure)

Abstract

This paper presents a new approach to speech enhancement from single-channel measurements involving both noise and channel distortion (i.e., convolutional noise), and demonstrates its applications for robust speech recognition and for improving noisy speech quality. 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 for speech estimation. Third, we present an iterative algorithm which updates the noise and channel estimates of the corpus data model. 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.
Original languageEnglish
Pages (from-to)1269-1286
Number of pages18
JournalComputer Speech & Language
Volume28
Issue number6
Early online date26 Apr 2014
DOIs
Publication statusPublished - Nov 2014

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