Frequency-warped time-weighted linear prediction for glottal vocoding

Auto-regressive modeling is a prevalent source-filter separation method of speech. Conventional linear prediction (LP) and its derivatives such as weighted linear prediction (WeLP) produce parametric spectral models within a linear frequency scale, whereas frequency-warped linear prediction (WaLP) can be used to take into account the frequency sensitivity of the human auditory system. From the perspective of glottal vocoding, the principles behind WeLP have been found to be beneficial for an accurate separation of the glottal source signal and the vocal tract transfer function, but this approach can not utilize the auditory benefits of frequency warping. On the other hand, the WaLP approach suffers from less accurate source-filter separation properties. In this study, a generalized frequency-warped time-weighted linear prediction (WWLP) analysis is proposed. Experiments with WWLP are performed within the context of glottal vocoding. The subjective listening test results show that WWLP-based spectral envelope modeling is able to increase quality over previously developed methods in some of the test cases.