Estimation of the Optimal HMM Parameters for Amazigh Speech Recognition System Using CMU-Sphinx

In this paper, we are looking for the optimal value of number of Hidden Markov Models (HMMs) states, and number of Gaussian mixture density functions for Amazigh automated speech recognition system. This system is based on the open source CMU Sphinx-4, from the Carnegie Mellon University. CMU Sphinx is a large-vocabulary; speaker-independent, continuous speech recognition system based on HMMs. the acoustic model is generally an HMMs, typically a three-state left-right HMM called Bakis It is perfectly adapted to the speech in its temporal progress since. the corpus of training consists of 11220 audio files. The test-data used for evaluating the system-performance consists of 1320 audio files. The performance of ASR is evaluate by the Word Error Rate WER, and results are compared to previous work.