Sub-segmental, segmental and supra-segmental analysis of linear prediction residual signal for language identification

Abstract

In this work, excitation source information is explored for language identification (LID) task. The excitation signal is represented by linear prediction (LP) residual. Different aspects of the excitation source information can be captured by processing LP residual signal at sub-segmental, segmental and supra-segmental levels. Gaussian mixture modelling (GMM) technique is used to build the language models. Present LID study has been carried out on IITKGP-MLILSC speech database. Individually, the segmental level information provides good LID accuracy followed by sub-segmental and supra-segmental level information. Combined evidences from all three levels represent the complete excitation source information. Finally, a comparative study has been carried out between the vocal tract and excitation source features, which portrays the distinct nature of these two features. Combination of both the features, yield an improvement of 10.01% in LID accuracy than only excitation source information. This observation indicates the significance of excitation source information for LID task.