Kazakh and Russian Languages Identification Using Long Short-Term Memory Recurrent Neural Networks

Abstract

Automatic language identification (LID) belongs to the automatic process whereby the identity of the language spoken in a speech sample can be distinguished. In recent decades, LID has made significant advancement in spoken language identification which received an advantage from technological achievements in related areas, such as signal processing, pattern recognition, machine learning and neural networks. This work investigates the employment of Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) for automatic language identification. The main reason of applying LSTM RNNs to the current task is their reasonable capacity in handling sequences. This study shows that LSTM RNNs can efficiently take advantage of temporal dependencies in acoustic data in order to learn relevant features for language recognition tasks. In this paper we show results for conducted language identification experiments for Kazakh and Russian languages and the presented LSTM RNN model can deal with short utterances (2s). The model was trained using open-source high-level neural networks API Keras on limited computational resources.