Improvement of the Speech Recognition under the Noisy Environment using the Wavelet Transform

Signal pattern recognition is one of the important technologies in this century. Speech recognition is very important for human interface with computer and machine. However, these speech recognition methods have a weak point, they work best in a noiseless condition. Automatic speech recognition systems are most effective in noiseless environments. If the data are polluted with noise, these speech recognitions are extremely difficult. For the noise reduction of signals, there are filters and spectral subtraction method, and so on. However, there is some limitation in case that the quality of the signal is poor. We propose a method for automatic speech recognition with signals contaminated with colored noise using modification of the spectral envelope shape. The proposed method is based on cepstral analysis. We have proposed the modified rules for adding valleys and recovering valleys. There is plenty of scope for improvement. In this paper, we apply the wavelet transform to the modification of spectral envelope shape. The wavelet transform is used for the extraction of particular features in the frequency and time domains. The wavelet transform is widely used for wave and image analysis. We apply a wavelet transform to speech recognition under noisy environments using cepstral analysis. As a result, speech recognition rate is improved. And data is compressed by the wavelet transform. It was shown that the wavelet analysis was one of the promising methodologies for the pattern recognition noisy signal.