An Efficient Sentence-based Sentiment Analysis for Expressive Text-to-speech using Fuzzy Neural Network

Abstract

In recent years, speech processing has become an active research area in the field of signal processing due to the usage of automated systems for spoken language interface. In developed countries, the customer service with automated system in speech synthesis has been the recent trend. The existing automated speech synthesis systems have certain problems during the real time implementation such as lack of naturalness in output speech, lack of emotions and so on. In this study, the novel Text to Speech system is introduced along with the sentiment analysis in Tamil language. The input text is first classified into the positive, negative and neutral based on the emotions in the sentence then the text is converted into speech with emotions during TTS conversion. Existing approaches used neural network based classifiers for classification. But, neural networks have certain drawbacks in real time training. So, this research study uses Fuzzy Neural Network (FNN) to classify the sentence based on the emotions. The text to speech with sentiment analysis effective scheme which is evaluated using Doordarshan news Tamil dataset. The proposed scheme is implemented using MATLAB. This TTS system has several social applications, especially in railway stations where the announcements can be made through expressive speech.