WSOLA for Reconstruction of Prolonged Speech Signal
K B Drakshayini, M. A. Anusuya, H. Y. Vani · International Journal of Fuzzy Logic and Intelligent Systems · 2023
Stuttering is one of the most common fluency disorders across all age groups.In this work, we propose a novel approach for reconstructing speech signals after prolongation detection and correction phase by applying waveform similarity overlap-add (WSOLA).Further processing of speech signals after prolongation detection and correction ensures sufficient signal continuity at segment joins by requiring maximal similarity to the natural continuity of the input signal.This work presents a major contribution towards improving the quality of speech signals after prolongation detection and phase correction, with further processing of speech signals for feature extraction followed by classification and phase clustering.The results are analysed in WSOLA phase for differentiating results before and after reconstruction using metrics such as the signal-to-noise ratio and total harmonic distortion.The WSOLA results are analysed with respect to different parameters such as the window time and overlap ratio.Moreover, the proposed method is implemented on a wide variety of signals derived from the University College London Archive of Stuttered Speech (UCLASS) dataset.A recognition accuracy of 95% was observed for output signals processed using WSOLA and applied over the K-means, fuzzy C-mean and support vector machine classifiers.