Research On Variation Speech Recognition Technology Based On Cortex-A8

The brain is the most important organ of human beings,which dominates human consciousness and behavior.In order to make the machine also have human brain-like intelligence,we have researched and developed a new discipline called artificial intelligence,which allows the machine to simulate human intelligence and respond.At present,speech recognition and face identification are widely used in this subject.Speech recognition technology uses the feature parameters in speech and the correlation between syllables to achieve matching with the database for speech recognition.Compared with other artificial intelligence technologies,speech recognition is simple and fast.The variation speech refers to the interference caused to the target speech caused by the change of the environment noise,mood,and channel,so that the recognition rate of the speech recognition system decreases.This article examines the following two aspects:1.Endpoint detection means a lot to speech recognition,which can effectively reduce system complexity and improve performance.This paper first compares and analyzes commonly used endpoint detection methods: short-term autocorrelation function maximum method,proportional method,short-time energy method,and double threshold method.By analyzing the advantages and disadvantages of these two methods,this paper proposes an endpoint detection method based on Multiscale Entropy,which is an adaptive data-driven signal processing method that is based on the feature that the entropy of speech signal is less than white noise.According to this,white noise can be filtered out.At the same time,because of different energy of speech signal in different frequency bands,the instantaneous energy value of speech signal can be obtained and the signal can be decomposed into high frequency band containing complex information and low frequency band containing noise;2.Using the traditional Hidden Markov Model,the recognition rate is low under the low signal-to-noise ratio environment,and its training parameters easily tend to converge to the local minimum,resulting in a decrease in recognition rate.In this paper,genetic algorithm improved HMM speech recognition system,the use of GA's global search ability to re-evaluate the characteristics of hidden Markov parameters.After obtaining the re-evaluated model,the search is most similar to the unknown speech,so that the target can be more accurately identified.Experimental results show that the improved hidden Markov model has better convergence speed and optimization performance,and the recognition rate is increased by at least 1.23%.