Bottleneck Feature-Based Hybrid Deep Autoencoder Approach for Indian Language Identification

Abstract

Latest and emerging approaches are essential to resolve the communication barrier among different languages in speech processing. The automatic language identification system is developed to identify the spoken language from speech utterances. Feature selection is a very challenging task in language identification. In this paper, bottleneck feature-based hybrid deep autoencoder approach is proposed to identify the given speech signal with corresponding language features. In the proposed approach, initially Mel-frequency cepstral coefficients, linear prediction coefficients, and shifted delta coefficients features are directly extracted from multilingual speech utterances. Further, we extracted bottleneck feature from the bottleneck layer of the bottleneck deep neural network. Initially, recognition rate has been evaluated for each feature set to find out the best feature. Finally, the best feature along with other features is used as the input for deep autoencoder with softmax regression to identify the language based on class labels. The deep autoencoder is fine-tuned to reach the global optimum through Jaya optimization algorithm. To carry out the experiments, the recorded database is used for four Indian languages with special emphasis on northeastern languages. The experimental results demonstrate that the proposed hybrid approach using bottleneck feature with shifted delta coefficients is performing well with 97.10% accuracy. Moreover, experimental results also show that proposed hybrid approach gives superior outcome when compared with the baseline deep neural network-based approaches.