Enhanced Deep Hierarchical Long Short-Term Memory and Bidirectional Long Short-Term Memory for Tamil Emotional Speech Recognition using Data Augmentation and Spatial Features

Abstract

Neural networks have become increasingly popular for language modelling and within these large and deep models, overfitting, and gradient remains an important problem that heavily influences the model performance. As long short-term memory (LSTM) and bidirectional long short-term memory (BILSTM) individually solve long-term dependencies in sequential data, the combination of both LSTM and BILSTM in hierarchical gives added reliability to minimise the gradient, overfitting, and long learning issues. Hence, this paper presents four different architectures such as the Enhanced Deep Hierarchal LSTM & BILSTM (EDHLB), EDHBL, EDHLL & EDHBB has been developed. The experimental evaluation of a deep hierarchical network with spatial and temporal features selects good results for four different models. The average accuracy of EDHLB is 92.12%, EDHBL is 93.13, EDHLL is 94.14% & EDHBB is 93.19% and the accuracy level obtained for the basic models such as the LSTM, which is 74% and BILSTM, which is 77%. By evaluating all the models, EDHBL performs better than other models, with an average efficiency of 94.14% and a good accuracy rate of 95.7%. Moreover, the accuracy for the collected Tamil emotional dataset, such as happiness, fear, anger, sadness, and neutral emotions indicates 100% accuracy in a cross-fold matrix. Emotions such as disgust show around 80% efficiency. Lastly, boredom shows 75% accuracy. Moreover, the training time and evaluation time utilised by EDHBL is less when compared with the other models. Therefore, the experimental analysis shows EDHBL as superior to the other models on the collected Tamil emotional dataset. When compared with the basic models, it has attained 20% more efficiency.