NDLI: Unsupervised adaptation to new speakers in feature-based letter recognition

Content Provider	IEEE Xplore Digital Library
Author	Lasry, M. Stern, R.
Copyright Year	1984
Description	Author affiliation: Carnegie-Mellon University of Pittsburgh, Pennsylvania (Lasry, M.)
Abstract	This paper describes two new methods by which the CMU feature-based recognition system can learn the acoustical characteristics of individual speakers without feedback from the user. We have previously described how the system uses MAP techniques to update its estimates of the mean values of features used by the classifier in recognizing the letters of the English alphabet on the basis of a priori information and labelled observations. In the first of the new procedures described in this paper the system assumes a correct decision every time it classifies a new utterance with a sufficiently high confidence level. In the second new procedure the system adjusts its estimates of the means on the basis of their correlation with the average values of the features over all utterances. Experiments were conducted on two confusable sets of letters using both speaker adaptation procedures. In each case classification performance using the unsupervised estimation procedures could equal that obtained using speaker adaptation with feedback from the user, although which method provided the better performance depended on which set of letters was being classified.
Starting Page	21
Ending Page	24
File Size	149056
Page Count	4
File Format	PDF
DOI	10.1109/ICASSP.1984.1172566
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1984-03-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Loudspeakers Character recognition Humans Force feedback Computer science Automatic speech recognition Speech recognition Technological innovation US Department of Defense Monitoring
Content Type	Text
Resource Type	Article

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