Hybrid Fuzzy HMM System for Arabic Connectionist Speech Recognition

Abstract

In this paper, a new Arabic connectionist speech recognition system is presented. This recognition system is based on the combination of the fuzzy integral theory and Hidden Markov Model (HMM). In this context, the fuzzy integral is used to relax the independence assumptions that are necessary with probability functions. It was found that one particular case with the choice of fuzzy integral as the Choquet integral, fuzzy probability measure, and fuzzy intersection operator as multiplication, reduces the generalized fuzzy HMM to the classical HMM. The traditional HMM and the proposed Fuzzy HMM systems are implemented by computer simulation and a perfornance comparison is carried out using the CSLU toolkit. The CSLU toolkit is a research and development software environment that provides a powerful and flexible tool for research in the field of spoken language understanding. It is noticed that, there is some improvements in recognition accuracy in case of the Fuzzy BMM (FHMM) system over the classical HMM recognition system. The FHMM recognition system accuracy varies from 93.36% to 98.36% depending on the data set used whereas the classical HMM' accuracy varies from 91.27% to 94.60% for the same data sets.