Speech Recognition Investigation In Car Environments

The study of speech recognition has been under way for well over half a century. Speech recognition offers great convenience in people's live. In our country, car plays more and more important role in recently ten years. Cars change people's life greatly in many respects, but people like cars with lots of functions, so cars have more and more electrical devices. More electrical devices means more complex operations, But it is very dangerous for drivers to leave steering wheel to operate the electrical devices. Car electrical devices with speech control Human Machine Interface may be the best solution for this problem. Because in our country similar of speech recoginition is still placed in a blank stage, therefore our conducts the research in this aspect will be enable our contury filll blank in this area.Firstly we analysis the technical diffcults of speech recognition in car noise envirments, and give solution to these problems. Speech endpoint detection in car noise envirments is more diffcult than in pure speech. We investigate several popluar endpoint detection technologies, and find the weakness of these technologies. A new method named single-well function based adaptive subband entropy is chose to solve the problem, and it works well than other methods in car noise environment. In our special noise background, car horn is very similar to speech at spectrogram view, so speech is confused by cars' horn. A new method based on frequency subband variety is adopted to compensate it, and it works well.Secondly, speech contaminated by car noise has low recognition rate. To overcome this drawback, we study two popluar noise cancellations technology, which are spectral subtraction and power spectral subtraction. To use it in practice we study the detail of the pratical tips. Our system adopts spectral subtraction technology to achieve speech enhancement.Thirdly, we study speech features used in speech recognition. Speech feature used in speech recognition mainly are Linear Predictive Coefficients and Mel-Frequency Cesptral Coefficients. Because of the noisy environment, the noise masked by speech may not be heard, but it still influences the ratio of speech recognition. So we must get rid of it. In this paper, we utilize psychoacoustics to modify Mel-Frequency Cesptral Coefficients, and experiments show it can improve recognition ratio.After that, we study Dynamic Time Warpped and Hidden Markov Chain carefully. Also we investigate how to modify it to achieve a good performance, we adopt clustering of Hidden Markov Model and it gives the foundation of classifier, setup of experiments and speech feature.Lastly, we give out our experiments setup, speech database. Our experiments use Dynamic Time Warpped and Hidden Markov Chain as classifier. In Hidden Markov Chain experiments we use a new method to speed up the calculation without decrease the speech recognition ratio. Experiments prove that Hidden Markov Chain classifier adapts large vocabulary system, and has good speech recognition ratio than Dynamic Time Warpped classifier. Experiments results shows the speech recognition ratio can achieve 98%, so it can use as car electrical devices' Human Machine Interface, and it gives out a new method to achieve car safe and fills the blank in this area.