A Genetic Algorithm-aided Hidden Markov Model Topology Estimation for Phoneme Recognition of Thai Continuous Speech

The use of hidden Markov models (HMM) in many pattern recognition tasks is now very common. Like other pattern recognitions, most automatic speech recognition systems rely on HMM acoustic models. In such systems, recognition performances are significantly affected by their topologies. In this paper, we propose an HMM topology estimation approach for Thai phoneme recognition tasks whose process is divided into 2 stages. First, a set of suitable topologies are constructed by combinations of different objective functions and topology generation methods. Second, a genetic algorithm is deployed as the topology selection algorithm which considers global fitness and selects the most suitable topology from the candidates proposed in the previous stage for each phoneme. As a result, the well-trained topology yields a maximum of 4.36% error reduction over predefined left-to-right models. The estimated topologies still work well when the topology estimation was performed on speech utterances whose recording environments differ from the ones recognized.