NDLI: Applying N-best keyword search to continuous speech recognition for telecommunication-based applications

Content Provider	IEEE Xplore Digital Library
Author	Ming-Whei Feng
Copyright Year	1994
Description	Author affiliation: GTE Labs. Inc., Waltham, MA, USA (Ming-Whei Feng)
Abstract	An N-best keyword search algorithm was developed in a continuous speech recognizer which models vocabulary words as well as extraneous sounds and noise, to achieve high sentence accuracy. The continuous speech recognizer was developed for telecommunication-based applications which typically demand high sentence accuracy. Possible approaches for achieving high sentence accuracy include applying complicated speech modeling techniques or employing more knowledge sources when conducting the recognition search. An alternative solution is to first apply an N-best decoding search to obtain N sentence hypotheses using pre-selected knowledge source(s) and then re-score those hypotheses using other knowledge source(s) or models. The proposed N-best keyword search algorithm derives all keyword sentence hypotheses and the corresponding likelihood scores time-synchronously. We show that the algorithm guarantees to find all sentence hypotheses. To reduce the exponentially growing number of hypotheses, in practical implementation we applied empirically derived thresholds to prune the search. Recognition experiments were conducted on two speech corpora: TI Connected Digit Corpus and Road Rally Corpus, to show the effectiveness of the proposed method.<>
Starting Page	726
Ending Page	729
File Size	271082
Page Count	4
File Format	PDF
ISBN	078031865X
DOI	10.1109/SIPNN.1994.344809
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-04-13
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vocabulary Viterbi algorithm Keyword search Laboratories Hidden Markov models Speech recognition Probability Speech enhancement Maximum likelihood decoding Acoustic waves
Content Type	Text
Resource Type	Article

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