ICA-based hierarchical text classification for multi-domain text-to-speech synthesis

In the framework of multi-domain Text-to-Speech synthesis it is essential to (i) design a hierarchically structured database for allowing several domains in the same speech corpus and (ii) include a text classification module that, at run time, assigns the input sentences to a domain or set of domains from the database. In this paper, we present a hierarchical text classifier based on Independent Component Analysis (ICA), which is capable of (i) organizing the contents of the corpus in a hierarchical manner and (ii) classifying the texts to be synthesized according to the learned structure. The document organization and classification performance of our ICA-based hierarchical classifier are evaluated in several encouraging experiments conducted on a journalistic-style text corpus for speech synthesis in Catalan.