Deep Learning-Based Text-Independent Speaker Verification for Channel and Language Mismatch in Indian Scenarios

This work presents an innovative deep neural network-based speaker verification (SV) model. SV is an authentication process that confirms whether a speaker is authorized. Text Independent Speaker Verification identifies who is speaking, not what is being said. India’s diverse linguistic and cultural landscape introduces challenges such as dialect and accent variations, multilingualism, multi-speaker scenarios, and varying quality of voice data recorded. Literature hints that the EER of the speaker verification model shoots up if tested with multi-language and multi-channel data; we have used the IITG-MV dataset, which has these variations captured specially for Indian conditions. Currently, convolutional neural networks (CNNs) have shown encouraging results in speaker verification. Our work presents CNN combined with the LSTMs, where CNN is a feed-forward neural network, and LSTMs are a recurrent neural network. The pipeline extracts the acoustic characteristics from the input speech signal using MFCC and fed into models based on deep neural networks like VGG-16. However, the raw speech signal is fed into the SincNet model. The CNN-LSTMs model demonstrates significantly superior accuracy to SincNet and VGG16, making it the most effective approach for text-independent speaker verification in diverse Indian languages and channels. Despite using a basic deep neural network, the results demonstrate the efficiency of our proposed architecture. CNN-LSTM’s state-of-the-art performance is closer to that of the ECAPA-TDNN model.