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A speech synthesizer which sounds similar to a human voice is preferred over a robotic voice, and hence to increase the naturalness of a speech synthesizer an efficacious prosody model is imperative. Hence, this paper is focused on developing a prosody prediction model using sentiment analysis for a Tamil speech synthesizer. Two variations of prosody prediction models using SentiWordNet are experimented: one without a stemmer and the other with a stemmer. The prosody prediction model with a stemmer performs much more efficiently than the one without a stemmer as it tackles the highly agglutinative and inflectional words in Tamil language in a better way and is exemplified clearly, in this paper. The performance of the prosody prediction model with a stemmer has a higher classification accuracy of 77% on the test set in comparison to the 57% accuracy by the prosody model without a stemmer.

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The primary aim of Human-Computer Interaction (HCI) is to deliver the power of computers and communication systems to people in an easily accessible and understandable form. HCI in a person’s native/first language is always invigorate. Developing a Tamil Text-To-Speech (TTS) system will facilitate a convenient medium of interaction for people who speak Tamil language. This paper emphasizes on the development of pronunciation models, a vital component of a Tamil TTS. Developing a pronunciation model for Tamil is more arduous when compared to other languages due to the non-triviality between the letter to sound correspondence. Veritably, two syllable-based pronunciation models developed by us are discussed in this paper. First, is a syllable-centric rule-based pronunciation model that generates a well-founded training data which is ingrained into the second, Conditional Random Field (CRF) enforced model. It is evident that both of these models are dominions with a high Mean Similarity Score of 0.97 and 0.94 respectively in comparison to the other existing rule driven and data driven models in the literature. These syllable-based pronunciation models will enrich the performance of a Tamil TTS.