CUED Publications database

Training a supra-segmental parametric F0 model without interpolating F0

Latorre, J and Gales, MJF and Knill, K and Akamine, M (2013) Training a supra-segmental parametric F0 model without interpolating F0. In: UNSPECIFIED pp. 6880-6884..

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Combining multiple intonation models at different linguistic levels is an effective way to improve the naturalness of the predicted F0. In many of these approaches, the intonation models for suprasegmental levels are based on a parametrization of the log-F0 contours over the units of that level. However, many of these parametrisations are not stable when applied to discontinuous signals. Therefore, the F0 signal has to be interpolated. These interpolated values introduce a distortion in the coefficients that degrades the quality of the model. This paper proposes two methods that eliminate the need for such interpolation, one based on regularization and the other on factor analysis. Subjective evaluations show that, for a Discrete-cosine-transform (DCT) syllable-level model, both approaches result in a significant improvement w.r.t. a baseline using interpolated F0. The approach based on regularization yields the best results. © 2013 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:32
Last Modified: 22 May 2018 07:18