Noise Suppression Based on Multi-Model Compositions Using Multi-Pass Search with Multi-Label N-gram Models

Takatoshi JITSUHIRO  Tomoji TORIYAMA  Kiyoshi KOGURE  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E91-D   No.3   pp.402-410
Publication Date: 2008/03/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e91-d.3.402
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Robust Speech Processing in Realistic Environments)
Category: Noisy Speech Recognition
Keyword: 
speech recognition,  noise suppression,  model composition,  multi-pass search,  E-Nightingale project,  

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Summary: 
We propose a noise suppression method based on multi-model compositions and multi-pass search. In real environments, input speech for speech recognition includes many kinds of noise signals. To obtain good recognized candidates, suppressing many kinds of noise signals at once and finding target speech is important. Before noise suppression, to find speech and noise label sequences, we introduce multi-pass search with acoustic models including many kinds of noise models and their compositions, their n-gram models, and their lexicon. Noise suppression is frame-synchronously performed using the multiple models selected by recognized label sequences with time alignments. We evaluated this method using the E-Nightingale task, which contains voice memoranda spoken by nurses during actual work at hospitals. The proposed method obtained higher performance than the conventional method.