Improving phone duration modelling using support vector regression fusion

Alexandros Lazaridis (1) , Iosif Mporas (1) , Todor Ganchev (1) , George Kokkinakis (1) , Nikos Fakotakis (1)

1 ECE - Department of Electrical and Computer Engineering [Patras]

In the present work, we propose a scheme for the fusion of different phone duration models, operating in parallel. Specifically, the predictions from a group of dissimilar and independent to each other individual duration models are fed to a machine learning algorithm, which reconciles and fuses the outputs of the individual models, yielding more precise phone duration predictions. The performance of the individual duration models and of the proposed fusion scheme is evaluated on the American-English KED TIMIT and on the Greek WCL-1 databases. On both databases, the SVR-based individual model demonstrates the lowest error rate. When compared to the second-best individual algorithm, a relative reduction of the mean absolute error (MAE) and the root mean square error (RMSE) by 5.5% and 3.7% on KED TIMIT, and 6.8% and 3.7% on WCL-1 is achieved. At the fusion stage, we evaluate the performance of twelve fusion techniques. The proposed fusion scheme, when implemented with SVR-based fusion, contributes to the improvement of the phone duration prediction accuracy over the one of the best individual model, by 1.9% and 2.0% in terms of relative reduction of the MAE and RMSE on KED TIMIT, and by 2.6% and 1.8% on the WCL-1 database.

Mots clés: Duration modelling; parallel fusion scheme; phone duration prediction; support vector regression; text-to-speech synthesis

Domaines: Linguistique

Alexandros Lazaridis, Iosif Mporas, Todor Ganchev, George Kokkinakis, Nikos Fakotakis. Improving phone duration modelling using support vector regression fusion. Speech Communication, 2010, 53 (1), pp.85. ⟨10.1016/j.specom.2010.07.005⟩. ⟨hal-00699049⟩