In this paper, we introduce an efficient and previously unreported approach to enhance the quality of corrupted narrowband speech signal using joint Vector Taylor Series (VTS) and Bandwidth Extension (BWE) algorithms. First, feature vectors extracted from the noisy narr More
In this paper, we introduce an efficient and previously unreported approach to enhance the quality of corrupted narrowband speech signal using joint Vector Taylor Series (VTS) and Bandwidth Extension (BWE) algorithms. First, feature vectors extracted from the noisy narrowband signal have modified applying VTS technique. Then, the estimation of corresponding wideband features have derived from the compensated parameters using two different artificial BWE methods (Envelope prediction with GMM and Neural Network). Finally, the distance between the wideband feature vectors and their estimated values evaluated using Log Spectral Distortion (LSD) measurement criteria. The results of implementation clearly show the advantage of proposed idea to improve the quality of the contaminated speech. In addition, we show that artificial BWE of speech signal, based on the neural network envelope extension outperforms better results in comparison with the GMM algorithm.
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