Examining the ability of one-class classifier to ensure the spectral smoothness of concatenated units
Daniel Tihelka, Martin Grůber, Jindřich Matoušek, Markéta Jůzová · 2016
We present initial experiments with one-class classification, aimed at replacing the “classic” heuristics-based measures used to estimate the smoothness of units concatenated together within unit selection speech synthesizers. A set of spectral feature distances was computed between neighbouring frames in natural speech recordings, i.e. those representing natural joins, from which the per-vowel classifier was trained. For the evaluation, we carried out ad-hoc listening tests collecting several examples of smooth and discontinuous joins, against which the classifier is tested. In addition, we also plugged the classifier into our TTS system to verify that the technique is capable of replacing the classic approach in the generic unit selection procedure.