Hybrid System of Optimal Self Organizing Maps and Hidden Markov Model for Arabic Digits Recognition

Thanks to Automatic Speech Recognition (ASR), a lot of machines can nowadays emulate human being ability to understand and speak natural language. However, ASR problematic could be as interesting as it is difficult. Its difficulty is precisely due to the complexity of speech processing, which takes into consideration many aspects: acoustic, phonetic, syntactic, etc. Thus, the most commonly used technology, in the context of speech recognition, is based on statistical models. Especially, the Hidden Markov Models which are capable of simultaneously modeling frequency and temporal characteristics of the speech signal. There is also the alternative of using Neuronal Networks. But another interesting framework applied in ASR is indeed the hybrid Artificial Neural Network (ANN) and Hidden Markov Model (HMM) speech recognizer that improves the accuracy of the two models. In the present work, we propose an Arabic digits recognition system based on hybrid Optimal Artificial Neural Network and Hidden Markov Model (OANN/HMM). The main innovation in this work is the use of an optimal neural network to determine the optimal groups, unlike in classical Kohonen approach. The numerical results are powerful and show the practical interest of our approach. Key-Words: Automatic Speech Recognition, Hidden Markov Models, Self Organizing Maps, Vector Quantization.