Finding Relevant Features for Statistical Speech Synthesis Adaptation

Statistical speech synthesis (SSS) models typically lie in a very high-dimensional space. They can be used to allow speech synthesis on digital devices, using only few sentences of input by the user. However, the adaptation algorithms of such weakly trained models suffer from the high dimensionality of the feature space. Because creating new voices is easy with the SSS approach, thousands of voices can be trained and a Nearest-Neighbor (NN) algorithm can be used to obtain better speaker similarity in those limited-data cases. NN methods require good distance measures that correlate well with human perception. This paper investigates the problem of finding good low-cost metrics, i.e. simple functions of feature values that map with objective signal quality metrics. We show this is a ill-posed problem, and study its conversion to a tractable form. Tentative solutions are found using statistical analyzes. With a performance index improved by 36% w.r.t. a naive solution, while using only 0.77% of the respective amount of features, our results are promising. Deeper insights in our results are then unveiled using visual methods, namely high-dimensional data visualization and dimensionality reduction techniques. Perspectives on new adaptation algorithms, and tighter integration of data mining and visualization principles are eventually given.