Speech recognition via Hidden Markov Model and neural network trained by genetic algorithm
Shing‐Tai Pan, Ching‐Fa Chen, Jian-Hong Zeng · 2010
It is the goal of this paper to find a more suitable architecture for speech recognition to be implemented on a chip. This paper uses the Hidden Markov Model (HMM) and the Artificial Neural Networks (ANN) for speech recognition. The speech recognition algorithms are then implemented on the Field Programmable Gate Array (FPGA) chip for a comparison of speech recognition speed on hardware for HMM and ANN. In order to obtain a solution more close to the optimal solution for the parameters of ANN, this paper use genetic algorithm (GA) to train the ANN. It will be seen that the ANN trained by GA will get a better performance than that trained by gradient-descent method.