Adaptive Speech Enhancement Based on Classification of Voiced/Unvoiced Signal

At low SNR´s, the conventional wavelet enhancement algorithm may lose some useful ingredients of the speech, resulting in a low enhancement performance. To solve the problem, this paper presents an improved wavelet enhancement algorithm based on the classification of the voiced/unvoiced signal. First, we eliminate part of the noise through spectral subtraction algorithm and separate voiced signal from unvoiced signal according to its short-time energy. Second, the Wavelet Packet Transform (WPT) is made on unvoiced speech to prevent signal distortion. Then dynamic thresholds are applied to different wavelet analytical scales to avoid a much smoother signal waveform. Finally, a new adaptive threshold function is used to make up the disadvantages of soft and hard thresholding algorithm. Experiments show that the proposed method can remove much noise while keeping intelligibility of the reconstructed speech.