Use of a novel generalized fuzzy hidden Markov model for speech recognition

We discuss a type of hidden Markov model (HMM) based on fuzzy sets and fuzzy integral theory which generalizes the classical stochastic HMM. The Choquet integral is used as a fuzzy integral which relaxes one of the two independence assumptions that we had with the classical HMM. We apply this new model to speech recognition and compare the performance with the classical HMM. In this research, the main innovation is that this new generalized fuzzy HMM is applied for the first time to speech recognition. Due to the fuzziness of the model, an interesting gain can be observed in terms of a lower computation time.