Paper Info
Title
Semi-Supervised Enhancement And Suppression Of Self-Produced Speech Using Correspondence Between Air- And Body-Conducted Signals
Abstract
We propose a semi-supervised method for enhancing and suppressing self-produced speech recorded with wearable air- and body-conductive microphones. Body-conducted signals are robust against external noise and predominantly contain self-produced speech. As a result, these signals provide informative acoustical clues when estimating a linear filter to separate a mixed signal into self-produced speech and background noise. In a previous study, we proposed a blind source separation method for handling air- and body-conducted signals as a multi-channel signal. While our previously proposed method demonstrated the superior performance that can be achieved by using air- and body-conducted signals in comparison to using only air-conducted signals, the enhanced and suppressed air-conducted signals tended to be contaminated with the acoustical characteristics of the body-conducted signals due to the nonlinear relationship between these signals. To address this issue, in this paper, we introduce a new source model which takes into consideration the correspondence between these signals and incorporates them within a semi-supervised framework. Our experimental results reveal that this new method alleviates the negative effects of using the acoustical characteristics of the body-conducted signals, outperforming our previously proposed method, as well as conventional methods, under a semi-supervised condition.
Year
DOI
Venue
2020
10.23919/Eusipco47968.2020.9287512
28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020)
Keywords
DocType
ISSN
Self-produced speech, Semi-supervised speech, enhancement and suppression, Air- and body-conducted signals
Conference
2076-1465
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Moe Takada100.68
Shogo Saki200.34
Patrick Lumban Tobing3157.89
Tomoki Toda41874167.18