Transform-domain speech periodicity enhancement with adaptive coefficient weighting

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In our previous study of speech periodicity enhancement, the linear prediction residual signal was decomposed into periodic and aperiodic components using two-stage transforms. In the transform domain, the periodic component of the signal is concentrated and represented by a small portion of the coefficients. The respective coefficients were weighted and emphasized to enhance periodicity of the signal against noise. Fixed weights were used for different sets of the coefficients. With the fixed weights, it is observed that unvoiced and voiced-unvoiced transition signals are excessively attenuated and perceptible artificial periodicity are generated in these speech segments. In this study, we propose an adaptive weighting method. For voiced speech, the periodic component is strong and the respective transform coefficients shows a high energy level. In contrast, for unvoiced speech, periodicity is weak and the corresponding coefficients are small. The weights for the coefficients are adaptively adjusted according to the energy level of the periodic component. With the adaptive weights, the periodic component of voiced speech can be effectively emphasized and restored while the aperiodic parts in unvoiced speech are retained.