Towards improving the performance of language identification system for Indian languages

2014 ◽  
Author(s):  
Abitha Anto ◽  
K. T. Sreekumar ◽  
C. Santhosh Kumar ◽  
P. C. Reghu Raj
Keyword(s):  
Language Identification ◽  
Identification System ◽  
Indian Languages

scholarly journals HMM Based Language Identification from Speech Utterances of Popular Indic Languages Using Spectral and Prosodic Features

2021 ◽  
Vol 38 (2) ◽  
pp. 521-528
Author(s):  
Manchala Sadanandam
Keyword(s):  
Reference Model ◽  
Simple Procedure ◽  
Test Sample ◽  
Language Identification ◽  
Identification System ◽  
Human Beings ◽  
Indian Languages ◽  
Prosodic Features ◽  
Mel Frequency Cepstral Coefficients ◽  
Feature Vectors

Language identification system (LID) is a system which automatically recognises the languages of short-term duration of unknown utterance of human beings. It recognises the discriminate features and reveals the language of utterance that belongs to. In this paper, we consider concatenated feature vectors of Mel Frequency Cepstral Coefficients (MFCC) and Pitch for designing LID. We design a reference model one for each language using 14-dimensional feature vectors using Hidden Markov model (HMM) then evaluate against all reference models of listed languages. The likelihood value of test sample feature vectors given in the evaluation is considered to decide the language of unknown utterance of test speech sample. In this paper we consider seven Indian languages for the experimental set up and the performance of system is evaluated. The average performance of the system is 89.31% and 90.63% for three states and four states HMM for 3sec test speech utterances respectively and also it is also observed that the system gives significant results with 3sec test speech for four state HMM even though we follow simple procedure.

A hierarchical language identification system for Indian languages

2012 ◽  
Vol 22 (3) ◽  
pp. 544-553 ◽  
Author(s):  
S. Jothilakshmi ◽  
V. Ramalingam ◽  
S. Palanivel
Keyword(s):  
Language Identification ◽  
Identification System ◽  
Indian Languages

scholarly journals Development of Indian Spoken Language Identification System for Two Languages using MFCC Feature with Deep Neural Network

2020 ◽  
Vol 9 (7S) ◽  
pp. 43-45
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Language Translation ◽  
Spoken Language ◽  
Language Identification ◽  
Classification Model ◽  
Identification System ◽  
Speech Sample ◽  
Indian Languages ◽  
Language Recognition

Language is the ability to communicate with any person. Approximate number of spoken languages are 6500 in the world. Different regions in a world have different languages spoken. Spoken language recognition is the process to identify the language spoken in a speech sample. Most of the spoken language identification is done on languages other than Indian. There are many applications to recognize a speech like spoken language translation in which the fundamental step is to recognize the language of the speaker. This system is specifically made to identify two Indian languages. The speech data of various news channels is used that is available online. The Mel Frequency Cepstral Coefficients (MFCC) feature is used to collect features from the speech sample because it provides a particular identity to the different classes of audio. The identification is done by using MFCC feature in the Deep Neural Network. The objective of this work is to improve the accuracy of the classification model. It is done by making changes in several layers of the Deep Neural Network.

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