DocumentCode :
2022016
Title :
Keyword-spotting using SRI´s DECIPHER large-vocabulary speech-recognition system
Author :
Weintraub, Mitchel
Author_Institution :
SRI Int., Menlo Park, CA, USA
Volume :
2
fYear :
1993
fDate :
27-30 April 1993
Firstpage :
463
Abstract :
The application of the speaker-independent large-vocabulary CSR (continuous speech recognition) system DECIPHER to the keyword-spotting task is described. A transcription is generated for the incoming spontaneous speech by using a CSR system, and any keywords that occur in the transcription are hypothesized. It is shown that the use of improved models of nonkeyword speech with a CSR system can yield significantly improved keyword spotting performance. The algorithm for computing the score of a keyword combines information from acoustics, language, and duration. One key limitation of this approach is that keywords are only hypothesized if they are included in the Viterbi backtrace. This does not allow the system builder to operate effectively at high false alarm levels if desired. Other algorithms are being considered for hypothesizing good score keywords that are on high scoring paths. An algorithm for smoothing language model probabilities was also introduced. This algorithm combines small task-specific language model training data with large task-independent language training data, and provided a 14% reduction in test set perplexity.<>
Keywords :
learning (artificial intelligence); speech recognition; vocabulary; DECIPHER; Viterbi backtrace; acoustics; algorithm; continuous speech recognition; keyword spotting performance; language model probabilities; test set perplexity; training data; transcription;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on
Conference_Location :
Minneapolis, MN, USA
ISSN :
1520-6149
Print_ISBN :
0-7803-7402-9
Type :
conf
DOI :
10.1109/ICASSP.1993.319341
Filename :
319341
Link To Document :
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