Spoken Language Identification Using Convolutional Neural Network

Abstract

Language, the core of human civilization, is all about communication. This research explores the intricacies of Automatic Speech Recognition (ASR) systems, particularly Speech-to-Language Identification (SLID), in multilingual environments. It focuses on data collection, feature extraction, and language classification, using techniques such as Mel spectrograms, MFCC, and deep learning architectures like CNNs and RNNs. Our dataset is from Kaggle and the rest of them are real-time. Deep learning models, particularly CNNs and hybrid architectures like CRNN, show promising results in language identification. An audio speech recognition model is developed using deep learning techniques. Data is stored in Google Drive and accessed via Colab for model training. The model was trained on Mel spectrograms of audio data using deep learning techniques. Pre-trained models like VGG16, Efficientnet, GoogleNet, and DenseNet offer viable alternatives for audio recognition tasks. We are going to propose them on our pre-processed dataset. The performance of all models will be compared using standard metrics like accuracy, precision, recall, and F1 score. This research offers an in-depth analysis of SLID systems, showing their importance in communication across languages and proposing technologies that may be able to increase their accuracy and allow wider acceptance.