Autoassociative neural network models for language identification

The objective of this paper is to demonstrate the feasibility of automatic language identification (LID) systems, using spectral features. The powerful features of autoassociative neural network models are exploited for capturing the language specific features for developing the language identification system. The nonlinear models capture the complex distribution of spectral vectors in the feature space for developing system parameters. The LID system can be easily extended for more number of languages without any additional higher level linguistic information. Effectiveness of the proposed method is demonstrated for identification of speech utterances from four Indian languages.