Spoken Language Identification using CNN with Log Mel Spectrogram Features in Indian Context

Abstract

This study demonstrates a novel application of Log Mel Spectrogram coefficients to image classification via Convolutional Neural Networks (CNN). The acoustic features obtained as log mel spectrogram images are used in this article. Log mel spectrogram pictures, a novel technique, ensure that the system is noise-resistant and free of channel mismatch. The majority of Indian languages from our own dataset were used.With the use of auditory features integrated in CNN, we hope to quickly and accurately detect a language. InceptionV3 and Resnet50 models are also used in this study for performance analysis. When compared to the existing system, these approaches achieved significant improvements in language identification accuracy.