Document Detail


Voice conversion for enhancing various types of body-conducted speech detected with non-audible murmur microphone.
MedLine Citation:
PMID:  20330333     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Our proposed statistical voice conversion approach to enhancing various types of body-conducted speech detected with Non-Audible Murmur (NAM) microphone is presented in this talk. NAM microphone, one of the body-conductive microphones [Nakajima et al., IEICE Trans. Inf. and Syst., E89-D, 1-8 (2006)], enables us to detect various types of body-conducted speech such as extremely soft whisper, normal speech, and so on. Moreover, it is robust against external noise due to its noise-proof structure. To make speech communication more universal by effectively using these properties of NAM microphone, body-conducted speech enhancement techniques have been developed with a state-of-the-art statistical voice conversion algorithm [Toda et al., IEEE Trans. ASLP, 15, 2222-2235 (2007)]. The proposed techniques would bring a new paradigm to human-to-human speech communication: e.g., the use of body-conducted voiced speech for noise robust speech communication, the use of body-conducted unvoiced speech for silent speech communication, and the use of body-conducted artificial speech for speaking aid [T Toda et al., Proc. ICASSP (2009) pp. 3601-3604]. This talk gives an overview of these promising techniques and presents their applications. [This research was supported in part by MIC SCOPE.].
Authors:
Tomoki Toda
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  The Journal of the Acoustical Society of America     Volume:  127     ISSN:  1520-8524     ISO Abbreviation:  J. Acoust. Soc. Am.     Publication Date:  2010 Mar 
Date Detail:
Created Date:  2010-03-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7503051     Medline TA:  J Acoust Soc Am     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1815     Citation Subset:  IM    
Affiliation:
Graduate School of Information Sci., Nara Inst. of Sci. and Technol., Takayama-cho 8916-5, Ikoma-shi, Nara 630-0192 Japan, tomoki@is.naist.jp.
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