NDLI: Accent adaptation in speech user interface

Content Provider	IEEE Xplore Digital Library
Author	Tanabian, M.M. Goubran, R.A.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. & Syst. Eng., Carleton Univ., Ottawa, Ont., Canada (Tanabian, M.M.; Goubran, R.A.)
Abstract	This paper examines the impact of accent present in speech on the performance of speaker independent automatic speech recognition (ASR) systems. In this paper, we show that, the presence of accent in the speech can increase the error rate. We validate a fundamental assumption that a speaker independent ASR engine, trained by a variety of accents, performs poorer than an engine that is trained for a particular accent, when tested by the same accent. Based on the results, we propose a method to lower the recognition error rate and measure the improvement by first determining the accent of the utterance and then applying the appropriate ASR engine from a bank of engines trained for different accents. We show that applying this method, will results to an average decrease of 24% overall error rate. The results are encouraging for a future complementary work. The research was carried out on an HMM based speech recognizer and TIMIT database was used to train and test the ASR engine.
File Size	496750
File Format	PDF
ISBN	0780390415
DOI	10.1109/VECIMS.2005.1567572
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-18
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	User interfaces Automatic speech recognition Natural languages Engines Automobiles Loudspeakers Testing Error analysis Hidden Markov models Speech recognition
Content Type	Text
Resource Type	Article

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