Research And Implement On Isolated Mandarin Speech Recognition

Isolated speech recognition is easy to implement and has been a mature state of technique. It can be applied broadly in many fields and is the base of deeply researching on speech recognition. Currently Hidden Markov Model is the trend of speech recognition, and most of successful speech recognition systems are based on this technique. This paper researches on isolated speech recognition by implement of a basic Mandarin speech recognition system of small scale vocabulary, isolated words and speaker independence using VC++ on Windows platform.First, the paper focus on fundamentals of speech recognition, including: principle of speech recognition, basic knowledge of speech signal processing, and all kinds of methods of speech training and recognition. Then study theories of Hidden Markov Model and it's applications on speech recognition.Based on the basic theories, the paper has most works as follow:1) Accomplishes design and implement of a basic Mandarin speech recognition system of small scale vocabulary, isolated words and speaker independence using Continuous Density Hidden Markov Model, and makes an experiment on this system. Because it is difficult to process speech signal by VC++ developing the system, the paper doesn't select Mel Frequency Cepstrum Coefficient (MFCC) as Feature Parameters. It chooses LPC Mel Cepstrum Coefficient (LPCMCC) as Feature Parameters that is almost equal to MFCC and easier to compute.2) In the experiment, it finds that the end-point detection method of two thresholds is sensitive to noisy. It can't get exact results of the end-point detection when wave data contain some noisy. In order to solve this problem, the paper researches on the end-point detection of speech signal, and present an endpoint detection method based on dynamic frame and self-adaptive threshold.3) Analyzes the contribution of each dimension of MFCC and gives methods of resisting noisy for feature coefficient.4) Finally, the paper indicates the methods to improve speed and efficiency of the Baum-Welch algorithms to re-estimate parameters of HMM. When Using the Baum-Welch algorithms to train the HMM, the speech recognition system is slow and poor efficient. So, it is necessary to give optimistic methods.