Sentence-based sentiment analysis for expressive text-to-speech

Alexandre Trilla, Francesc Alias

Producció científica: Article en revista indexadaArticleAvaluat per experts

58 Cites (Scopus)


Current research to improve state of the art Text-To-Speech (TTS) synthesis studies both the processing of input text and the ability to render natural expressive speech. Focusing on the former as a front-end task in the production of synthetic speech, this article investigates the proper adaptation of a Sentiment Analysis procedure (positive/neutral/negative) that can then be used as an input feature for expressive speech synthesis. To this end, we evaluate different combinations of textual features and classifiers to determine the most appropriate adaptation procedure. The effectiveness of this scheme for Sentiment Analysis is evaluated using the Semeval 2007 dataset and a Twitter corpus, for their affective nature and their granularity at the sentence level, which is appropriate for an expressive TTS scenario. The experiments conducted validate the proposed procedure with respect to the state of the art for Sentiment Analysis.

Idioma originalAnglès
Número d’article6295649
Pàgines (de-a)223-233
Nombre de pàgines11
RevistaIEEE Transactions on Audio, Speech and Language Processing
Estat de la publicacióPublicada - 2013


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