Multilingual Speech Corpus in Low-Resource Eastern and Northeastern Indian Languages for Speaker and Language Identification

Research and development of speech technology applications in low-resource languages (LRL) are challenging due to the non-availability of proper speech corpus. Especially, for most of the Indian languages, the amount and type of data found in different digital sources are sparse and prior works are too few to serve the purpose of large-scale development needs. This paper illustrates the creation process of such an LRL corpus comprising of sixteen rarely studied Eastern and Northeastern (E&NE) Indian languages and presents the data variability with different statistics. Furthermore, several experiments are carried out using the collected LRL corpus to build baseline speaker identification (SID) and language identification (LID) system for acceptance evaluation. For investigating the presence of speaker and language-specific information, spectral features like Mel frequency cepstral coefficients (MFCCs), shifted delta cepstral (SDC), and relative spectral transform-perceptual linear prediction (RASTA-PLP) features are used here. Vector quantization (VQ), Gaussian mixture models (GMMs), support vector machine (SVM), and multilayer perceptron (MLP)-based models are developed to represent the speaker and language-specific information captured through the spectral features. Apart from this, i-vectors, time delay neural networks (TDNN), and recurrent neural network with long short-term memory (LSTM-RNN) method-based SID and LID models are being experimented with to comply with the recent approaches. Performances of the developed systems are analyzed with LRL corpus in terms of SID and LID accuracy. The best SID and LID performances are observed to be 94.49% and 95.69%, respectively, for the baseline systems using LSTM-RNN with MFCC + SDC feature.