A Modified Tunable – Q Wavelet Transform Approach for Tamil Speech Enhancement

Abstract

ABSTRACT In this paper, a new speech enhancement approach using the modified Tunable-Q Wavelet Transform (TQWT) for Tamil speaker recognition systems is proposed. Different forms of the existing wavelet transforms like continuous wavelet transform and discrete wavelet transform provide little ability to tune the Q factor and redundancy of the wavelet and have less reconstruction property. TQWT is fully discrete, has perfect reconstruction property and is modestly overcomplete. The Q-factor and redundancy can be easily tuned. Speech signals vary in both time and space. The entire speech signal is divided into different parts. Depending on the oscillatory contents of the speech signal, different Q factors are applied for each part instead of applying a single Q factor for the entire data. This gives more accuracy in decomposition. The proposed method is evaluated on several speakers and under various noise conditions including babble, street and exhibition noises. In order to tune the quality factor (Q) of the TQWT and to obtain good reconstruction property, speech enhancement has been conducted with varying values of Q. The reconstruction error, cost function, Signal to Noise Ratio(SNR) and Mean Opinion Score(MOS) show that the new approach highly improves the performance of speech enhancement based on the modified TQWT. Hardware implementation is also done using TMS320C6713 DSK and the results are similar to simulation.