Efficient methods for detecting keywords in continuous speech

This paper refers to our prosperous development of algorithms for detecting keywords in continuous speech. Two different approaches to define confidence measures are introduced. As an advantage, these definitions are theoretically calculable without artful tuning. Moreover, two distinct decoding algorithms are presented, that incorporate these confidence measures into the search procedure. One is a new possibility of detecting keywords in continuous speech, using the standard Viterbi algorithm without modeling the non-keyword parts of the utterance. The other one is an improved further development of an algorithm described in [1], also without the need of modeling the non-keyword parts.

[1]  Hervé Bourlard,et al.  Optimizing recognition and rejection performance in wordspotting systems , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[2]  Harald Höge,et al.  A new keyword spotting algorithm with pre-calculated optimal thresholds , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[3]  Michael Weintraub,et al.  Keyword-spotting using SRI's DECIPHER large-vocabulary speech-recognition system , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Eduardo Lleida,et al.  Likelihood ratio decoding and confidence measures for continuous speech recognition , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.