Spoken language identification for Indian languages using split and merge EM Algorithm

Authors:
Naresh Manwani;Suman K. Mitra;M. V. Joshi
Affiliations:
Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India;Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India;Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India
Venue:
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Year:
2007

Citing 3
Cited 0

A segmental approach to automatic language identification

A segmental approach to automatic language identification
SMEM Algorithm for Mixture Models

Neural Computation
A model-selection-based self-splitting Gaussian mixture learning with application to speaker identification

EURASIP Journal on Applied Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Performance of Language Identification (LID) System using Gaussian Mixture Models (GMM) is limited by the convergence of Expectation Maximization (EM) algorithm to local maxima. In this paper an LID system is described using Gaussian Mixture Models for the extracted features which are then trained using Split and Merge Expectation Maximization Algorithm that improves the global convergence of EM algorithm. It improves the learning of mixture models which in turn gives better LID performance. A maximum likelihood classifier is used for classification or identifying a language. The superiority of the proposed method is tested for four languages.