Spoken Language Identification using Deep Learning

Title

Spoken Language Identification using Deep Learning

Creator

Julius C.A.; Vijayalakshmi S.; Palathara T.S.

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.

Source

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

deep neural network; Language identification; major indian spoken languages; mel-frequency cepstral coefficients; spoken language

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

Rights

Restricted Access

Relation

ISBN: 979-835037809-2

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICEECT61758.2024.10738935

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211124095&doi=10.1109%2fICEECT61758.2024.10738935&partnerID=40&md5=2c5665158c18ca315fc898d2d0ac9a1e