| Paper: | MLSP-P4.8 | ||
| Session: | Machine Learning Applications | ||
| Time: | Thursday, May 20, 09:30 - 11:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | ICA-BASED HIERARCHICAL TEXT CLASSIFICATION FOR MULTI-DOMAIN TEXT-TO-SPEECH SYNTHESIS | ||
| Authors: | Xavier Sevillano; Enginyeria i Arquitectura La Salle, Universitat Ramon Llull | ||
| Francesc Alías; Enginyeria i Arquitectura La Salle, Universitat Ramon Llull | |||
| Joan Claudi Socoró; Enginyeria i Arquitectura La Salle, Universitat Ramon Llull | |||
| Abstract: | 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. | ||
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