NDLI: Mispronunciation detection via dynamic time warping on deep belief network-based posteriorgrams

Content Provider	IEEE Xplore Digital Library
Author	Ann Lee Yaodong Zhang Glass, J.
Copyright Year	2013
Description	Author affiliation: Artificial Intell. Lab., MIT Comput. Sci., Cambridge, MA, USA (Ann Lee; Yaodong Zhang; Glass, J.)
Abstract	In this paper, we explore the use of deep belief network (DBN) posteriorgrams as input to our previously proposed comparison-based system for detecting word-level mispronunciation. The system works by aligning a nonnative utterance with at least one native utterance and extracting features that describe the degree of mis-alignment from the aligned path and the distance matrix. We report system performance under different DBN training scenarios: pre-training and fine-tuning with either native data only or both native and nonnative data. Experimental results have shown that by substituting the system input from MFCC or Gaussian posteriorgrams obtained in a fully unsupervised manner to DBN posteriorgrams, the system performance can be improved by at least 10.4% relatively. Moreover, the system performance remains steady when only 30% of the annotations being used.
Starting Page	8227
Ending Page	8231
File Size	279533
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6639269
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech Training Feature extraction System performance Mel frequency cepstral coefficient Speech recognition Support vector machines deep belief networks mispronunciation detection dynamic time warping
Content Type	Text
Resource Type	Article

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