| Summary: | 碩士 === 國立成功大學 === 資訊管理研究所 === 94 === In recent years, technology of speech recognition has been used in many situations, especially in management communication. Speech can improve the communication effectiveness among people and computers. Hence, interface of speech recognition is one of the most important research areas. Speech recognition systems usually use parameters obtained from training noise-free samples. Therefore, in noise environment various additive noises will deteriorate speech recognition results. To solve this problem, extra processes are requited to improve accuracy of recognition. In this study we use Signal Bias Removal method to estimate the bias of noise and then remove the bias of the speech parameters to minimize undesirable effects.
In this study, we also use machine learning and statistic methods for speech recognition. Feature analysis with the clean speech data is proceeded through a series processes, such as: digitization、frameing、endpoint detection、pre-emphasis、hamming window、mfcc. After those steps, we identify speech parameters from training Hidden Markov Models. During recognition phase, an estimate of the bias was computed for each test utterance and subtracted from it. These approaches minimize mismatches and reach better result of recognition accuracy.
In the process of Signal Bias Removal, We choose several different noises(white noise、babble noise、car noise、high frequency noise and factory noise) and different SNR for the experiment. The results of experiments show improvement of speech accuracy. One advantage of the SBR method over other methods is that it can be employed during the testing phase alone.
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