Implementation of Embedded Unspecific Continuous English Speech Recognition Based on HMM

Abstract

Background: To improve the usage of computers, a very important role is played by the Human-computer interaction through Natural Language Conversational Interfaces. Speech recognition technology allows the machine to understand human language. To achieve this function, a speech recognition algorithm is used. Methods: In order to realize the embedded speech recognition function based on HMM under the ARM platform, this paper, mainly based on the basic theoretical research of speech signals, establishes the HMM model, uses speech collection, recognition and other methods, simulates on MATLAB, and integrates the recognition system ported to ARM for debugging and running. Results: The research results show that the accuracy of HMM model experimental recognition can reach 98%, and the speed of speech recognition simulation on ARM is faster than speech recognition. The innovation of this paper is to make a comprehensive system introduction to embedded speech recognition from the perspective of embedded systems, and perform speech recognition simulation on MATLAB. Conclusion: The conclusion shows that the HMM-based embedded unspecific continuous English speech recognition system has high recognition accuracy and fast speed.