A DCRNN-based ensemble classifier for speech emotion recognition in Odia language

The Odia language is an old Eastern Indo-Aryan language, spoken by 46.8 million people across India. We have designed an ensemble classifier using Deep Convolutional Recurrent Neural Network for Speech Emotion Recognition (SER). This study presents a new approach for SER tasks motivated by recent research on speech emotion recognition. Initially, we extract utterance-level log Mel-spectrograms and their first and second derivative (Static, Delta, and Delta-delta), represented as 3-D log Mel-spectrograms. We utilize deep convolutional neural networks deep convolutional neural networks to extract the deep features from 3-D log Mel-spectrograms. Then a bi-directional-gated recurrent unit network is applied to express long-term temporal dependency out of all features to produce utterance-level emotion. Finally, we use ensemble classifiers using Softmax and Support Vector Machine classifier to improve the final recognition rate. In this way, our proposed framework is trained and tested on Odia (Seven emotional states) and RAVDESS (Eight emotional states) dataset. The experimental results reveal that an ensemble classifier performs better instead of a single classifier. The accuracy levels reached are 85.31% and 77.54%, outperforming some state-of-the-art frameworks on the Odia and RAVDESS datasets.