NDLI: Fast on-line adaptation using KSVD based acoustic clustering

Content Provider	IEEE Xplore Digital Library
Author	Shahnawazuddin, S. Sinha, R.
Copyright Year	2013
Description	Author affiliation: Dept. of Electron. & Electr. Eng., Indian Inst. of Technol. Guwahati, Guwahati, India (Shahnawazuddin, S.; Sinha, R.)
Abstract	In this work, the issues of on-line adaptation for real-time applications are addressed. In such systems, unsupervised adaptation has to performed with a very small amount of adaptation data. Furthermore, in such tasks, the computational complexity involved should be as low as possible to keep the system latency in check. To address both these issues, a model interpolation based fast adaptation procedure, employing speaker cluster models as bases, is presented in this work. It is observed that the acoustic clustering of the training speakers to derive the bases greatly reduces the complexity in comparison to the techniques which employ speaker adapted models as bases. Apart from this, a KSVD based acoustic clustering scheme is also proposed. Acoustic clustering in supervised as well unsupervised mode is explored in this work. The proposed on-line adaptation procedure employing the KSVD clustering, is found to result in a relative improvement of 6% in WER on an LVCSR task.
Starting Page	1
Ending Page	5
File Size	1098301
Page Count	5
File Format	PDF
e-ISBN	9781479922758
DOI	10.1109/INDCON.2013.6725938
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-13
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Adaptation models Interpolation acoustic clustering Dictionaries Hidden Markov models fast adaptation Data models Acoustics on-line adaptation Automatic speech recognition
Content Type	Text
Resource Type	Article

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