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Communication Dans Un Congrès Année : 2013

Belief Hidden Markov Model for speech recognition

Résumé

Speech Recognition searches to predict the spoken words automatically. These systems are known to be very expensive because of using several pre-recorded hours of speech. Hence, building a model that minimizes the cost of the recognizer will be very interesting. In this paper, we present a new approach for recognizing speech based on belief HMMs instead of proba-bilistic HMMs. Experiments shows that our belief recognizer is insensitive to the lack of the data and it can be trained using only one exemplary of each acoustic unit and it gives a good recognition rates. Consequently, using the belief HMM recognizer can greatly minimize the cost of these systems.
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Dates et versions

hal-01108027 , version 1 (22-01-2015)

Identifiants

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Siwar Jendoubi, Boutheina Ben Yaghlane, Arnaud Martin. Belief Hidden Markov Model for speech recognition. International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), Apr 2013, Hammamet, Tunisia. pp.1 - 6, ⟨10.1109/ICMSAO.2013.6552563⟩. ⟨hal-01108027⟩
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