LAUSR

Linear prediction (LP) is a prevalent source-filter separation method of speech production. One of the drawbacks of conventional LP-based approaches is the biasing of estimated formants by harmonic peaks. Methods such as discrete all-pole modeling and weighted LP have been proposed to overcome this problem, but they all use a linear frequency scale. This study proposes a new LP technique, frequency-warped time-weighted linear prediction (WWLP), to provide spectral envelope estimates robust to harmonic peaks that work on a warped frequency scale that approximates the sensitivities of the human auditory system. Experiments are performed within the context of vocoding in statistical parametric speech synthesis. Subjective listening test results show that WWLP-based spectral envelope modeling increases quality over previously developed methods.