  Language Identification (LID) is a crucial preliminary process in the field
of Automatic Speech Recognition (ASR) that involves the identification of a
spoken language from audio samples. Contemporary systems that can process
speech in multiple languages require users to expressly designate one or more
languages prior to utilization. The LID task assumes a significant role in
scenarios where ASR systems are unable to comprehend the spoken language in
multilingual settings, leading to unsuccessful speech recognition outcomes. The
present study introduces convolutional recurrent neural network (CRNN) based
LID, designed to operate on the Mel-frequency Cepstral Coefficient (MFCC)
characteristics of audio samples. Furthermore, we replicate certain
state-of-the-art methodologies, specifically the Convolutional Neural Network
(CNN) and Attention-based Convolutional Recurrent Neural Network (CRNN with
attention), and conduct a comparative analysis with our CRNN-based approach. We
conducted comprehensive evaluations on thirteen distinct Indian languages and
our model resulted in over 98\% classification accuracy. The LID model exhibits
high-performance levels ranging from 97% to 100% for languages that are
linguistically similar. The proposed LID model exhibits a high degree of
extensibility to additional languages and demonstrates a strong resistance to
noise, achieving 91.2% accuracy in a noisy setting when applied to a European
Language (EU) dataset.
