Spoken Language Identification using Deep Learning
subject: deep neural network; Language identification; major indian spoken languages; mel-frequency cepstral coefficients; spoken language
description: A crucial problem in natural language processing is language identification, which has applications in speech recognition, translation services, and multilingual content. The five main Indian languages that are the subject of this study are Hindi, Bengali, Tamil, English, and Gujarati. A Deep Neural Network is introduced in the paper which is specifically made to use Mel-Frequency Cepstral Coefficients (MFCCs) for sophisticated language categorization. The suggested architecture of the model, which includes batch normalisation and tightly linked layers, helps it to be adept at identifying complex linguistic patterns. Comparing the research to the source work [18], promising improvements are shown, highlighting the potential of the model in language detection. 2024 IEEE.
creator: Julius C.A.; Vijayalakshmi S.; Palathara T.S.
source: 2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835037809-2
format: Online
language: English
type: Conference paper
coverage: Julius C.A., Christ University, Deparment of Data Science, Lavasa, Pune, India; Vijayalakshmi S., Christ University, Deparment of Data Science, Lavasa, Pune, India; Palathara T.S., Christ University, Deparment of Data Science, Lavasa, Pune, India