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ICASSP
2011
IEEE

Acoustic model training for non-audible murmur recognition using transformed normal speech data

9 years 2 months ago
Acoustic model training for non-audible murmur recognition using transformed normal speech data
In this paper we present a novel approach to acoustic model training for non-audible murmur (NAM) recognition using normal speech data transformed into NAM data. NAM is extremely soft murmur, that is so quiet that people around the speaker can hardly hear it. It is detected directly through the soft tissue of the head using a special body-conductive microphone, NAM microphone, placed on the neck below the ear. NAM recognition is one of the promising silent speech interfaces for man-machine speech communication. We have previously shown the effectiveness of speaker adaptive training (SAT) based on constrained maximum likelihood linear regression (CMLLR) in NAM acoustic model training. However, since the amount of available NAM data is still small, the effect of SAT is limited. In this paper we propose modified SAT methods capable of using a larger amount of normal speech data by transforming them into NAM data. The experimental results demonstrate that the proposed methods yield an ab...
Denis Babani, Tomoki Toda, Hiroshi Saruwatari, Kiy
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Denis Babani, Tomoki Toda, Hiroshi Saruwatari, Kiyohiro Shikano
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