Automatic Speech Recognition and Translation for Low Resource Languages

1A Hybrid Deep Learning Model for Emotion Conversion in Tamil Language

Satrughan Kumar Singh¹*, Muniyan Sundararajan² and Jainath Yadav¹

¹Department of Computer Science, Central University of South Bihar, Gaya, Bihar, India

²Department of Mathematics and Computer Science, Mizoram University, Aizawl, Mizoram, India

Abstract

In speech signal processing, emotion recognition is a challenging task in classifying speech into different emotions. In this chapter, we propose a hybrid model based on FFNN (feed forward neural network) and SVM (support vector machine) for automated emotion conversion in the Tamil language. The use of voice command indeed contributes to a better integrated human-machine interface integration where one can give voice command, which intelligent machine understands and obeys. The Tamil language is mostly syllabic for the synthetical analysis of speech signal recognition. The changes in speech signal processing are mainly observed in several acoustic parameters such as root mean square energy, short-time energy, mel-frequency cepstral coefficient, and zero crossing rate, which are subsequently used for discrimination of the generation of a new set of the feature vector. In this proposed model, firstly, the FFNN model is complemented on the training and test datasets. Thereafter, SVM is used to perform the classification task. In the proposed emotion transformation, emotions such as angry, happy, sad, calm, surprised, fearful, neutral, and disgust are considered ...