No abstract

This paper proposes an adaptive fuzzy wavelet filter that is based on a fuzzy inference system for enhancing speech signals and improving the accuracy of speech recognition. In the last two decades, the basic wavelet thresholding algorithm has been extensively used for noise filtering. In the proposed method, adaptive wavelet thresholds are generated and controlled according to the fuzzy rules about the presence of speech in contaminated signals. In this adaptive fuzzy wavelet filter, the relationships between speech and noise are summarized into seven fuzzy rules using four linguistic variables, which are used to determine the state of a signal. A hybrid filter is proposed here, which combines an adaptive fuzzy wavelet filter and the spectral subtraction method to filter contaminated signals. An amplified voice activity detector in the proposed hybrid filter is designed to improve performance when the signal-to-noise ratio (SNR) is lower than 5 dB. The filtering that is performed using the adaptive fuzzy wavelet filter and the spectral subtraction method is controlled by support vector machines. Experimental results demonstrate that the proposed system effectively increases the SNR and the speech recognition rate.