Wavelet-based techniques for speech recognition

In this thesis, new wavelet-based techniques have been developed for the extraction of features from speech signals for the purpose of automatic speech recognition (ASR). One of the advantages of the wavelet transform over the short time Fourier transform (STFT) is its capability to process non-stationary signals. Since speech signals are not strictly stationary the wavelet transform is a better choice for time-frequency transformation of these signals. In addition it has compactly supported basis functions, thereby reducing the amount of computation as opposed to STFT where an overlapping window is needed. [Continues.

Perceptually Motivated Wavelet Packet Transform for Bioacoustic Signal Enhancement

A significant and often unavoidable problem in bioacoustic signal processing is the presence of background noise due to an adverse recording environment. This paper proposes a new bioacoustic signal enhancement technique which can be used on a wide range of species. The technique is based on a perceptually scaled wavelet packet decomposition using a species-specific Greenwood scale function. Spectral estimation techniques, similar to those used for human speech enhancement, are used for estimation of clean signal wavelet coefficients under an additive noise model. The new approach is compared to several other techniques, including basic bandpass filtering as well as classical speech enhancement methods such as spectral subtraction, Wiener filtering, and Ephraim–Malah filtering. Vocalizations recorded from several species are used for evaluation, including the ortolan bunting (Emberiza hortulana), rhesus monkey (Macaca mulatta), and humpback whale (Megaptera novaeanglia), with both additive white Gaussian noise and environment recording noise added across a range of signal-to-noise ratios (SNRs). Results, measured by both SNR and segmental SNR of the enhanced wave forms, indicate that the proposed method outperforms other approaches for a wide range of noise conditions

A New Wavelet Denoising Method for Noise Threshold

A new method is used wavelet 1-D experimental signal for denoising. It is provided the optimal adaptive threshold of sub-band based on input signals. The new method: 1) use a new method with low complexity that calculates thresholds; 2) use threshold for each sub-bands; 3) divide three sub-band with range of human hearing and range of the hearing tests are often displayed in the form of an audiogram; 4) use a new denoising algorithm depends on attribute of signal for wavelet coefficients; 5) applies denoising to the detail coefficients. The new method called Adaptive Thresholding with Mean for hybrid Denoising method of hard and soft function (ATMDe) and applied to hearing loss and it is found that it increases the signal-to-noise ratio by more than 114 % and decreases the mean-square-error (MSE). The result of new method with SNR and MSE is higher than standard denoising methods. Hence, the new method was found that has good performance and adaptive threshold value is better than other methods.This study is proposed a new adaptive threshold based on noisy speech for each sub-bands with low complex and it is suitability for range of human hearing and range of hearing test. A new method is used wavelet 1-D experimental signal for denoising. It provided the optimal adaptive threshold of three sub-band with applies to the detail coefficients. The speech enhancement is used of threshoding on the adpated wavelet coefficients, and the results are compared a variety of noisy speech and four well-known benchmark signals. The results, measured objectively by Signal-to-Noise ratio (SNR) and Mean Square Error (MSE), are given for additive white Gaussian noise as well as two different types of noisy environment. The new method called Adaptive Thresholding with Mean for hybrid Denoising method of hard and soft function (ATMDe) and applied to hearing loss and it is found that it increases the signal-to-noise ratio by more than 114% and decreases the mean-square-error (MSE). The result of new method with SNR and MSE is higher than standard denoising methods. Hence, the new method was found that has good performance and adaptive threshold value is better than other methods