Belief Hidden Markov Model for speech recognition
  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.
