| Paper: | SP-P13.15 (ICASSP 2003 Paper) | ||
| Session: | General Topics in Robust Speech Recognition | ||
| Time: | Thursday, May 20, 13:00 - 15:00 | ||
| Presentation: | Poster (ICASSP 2003 Presentation) | ||
| Topic: | Speech Processing: Confidence Measures/Rejection | ||
| Title: | CONFIDENCE MEASURES FOR KEYWORD SPOTTING USING SUPORT VECTOR MACHINES | ||
| Authors: | Yassine Benayed; LORIA | ||
| Dominique Fohr; LORIA | |||
| Jean Paul Haton; LORIA | |||
| Gerard Chollet; ENST CNRS-LTCI | |||
| Abstract: | Support Vector machines (SVM) is a new and very promisingclassification technique developed from the theory of Structural RiskMinimisation. In this paper, we propose an alternativeout-of-vocabulary word detection method relying on confidence measuresand support vector machines. Confidence measures are computed fromphone level information provided by a Hidden Markov Model (HMM) basedspeech recognizer. We use three kinds of average techniques asarithmetic, geometric and harmonic averages to compute a confidencemeasure for each word. The acceptance/rejection decision of a word isbased on the confidence feature vector which is processed by a SVMclassifier. The performance of the proposed SVM classifier iscompared with methods based on the averaging of confidence measures. | ||
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