Improvement of the noisy speech recognition using method of thresholds and emphasizing wavelet coefficients for clean speech

Abstract

In recent years, speech recognition under the noisy environment is one of the very important technologies. This study improves a speech recognition method for the signals under the noisy environment using method of thresholds and emphasizing wavelet coefficients for clean signal data. Noisy speech recognitions are extremely difficult problem. To analyze noise problem, in general, the majority of people have used the Fourier analysis. But the Fourier transform reveals only the frequency information. The general noise filters reduce specific frequency band contained both signal and noise. It is difficult to eliminate only noise component from a signal containing noise components. To overcome this difficulty, we applied the wavelet analysis. In this study, we improve the speech recognition under the noisy environment by bringing it close to reference data and noisy input data by modifying the spectrum by the wavelet transform using thresholds and emphasizing methods for reference clean speech data. We apply this method using wavelet transform to the modification of spectral envelope shape. As a result, noisy speech recognition rate is improved by this method using wavelet transform for clean speech data.

Small-Footprint Wake Up Word Recognition in Noisy Environments Employing Competing-Words-Based Feature

“…Recently, the attention method was applied to small-footprint keyword recognition [10][11][12]. Various studies have also been conducted to improve recognition performance in noisy environments [13][14][15][16].…”