Research on Speech Recognition Based on Neural Networks

Because of good characteristics of the abstract classification, neural networks have become an effective tool for resolving issues related to recognition, and have been applied to the research and development of speech recognition system. A speech recognizer system comprises of two blocks, Feature Extractor and Recognizer. For increasing the recognition accuracy, this paper proposes two types of speech recognition system whose recognition block uses the recurrent neural network(RNN) and multi layer perceptron(MLP) respectively. Furthermore, the main work steps of Feature Extractor (FE) block is introduced and the structure of two types of neural networks mentioned above is discussed. Using a standard LPC Cepstrum, the FE translates the input speech into a trajectory in the LPC Cepstrum feature space. The recognizer block discovers the relationships between the trajectories and recognizes the word. The results show that the MLP's recognition accuracies were better than the RNN's,while the RNN's recognition accuracies achieved 85%.