Abstract
In this paper, we examine the effectiveness of prosodic features for language identification. Prosodic differences among world languages include variations in intonation, rhythm, and stress. These variations are represented using features derived from fundamental frequency (F0) contour, duration, and energy contour. For extracting the prosodic features, speech signal is segmented into syllable-like units by locating vowel-onset points (VOP) automatically. Various parameters are then derived to represent F0 contour, duration, and energy contour characteristics for each syllable-like unit The features obtained by concatenating the parameters derived from three consecutive syllable-like units are used to represent the prosodic characteristics of a language. The prosodic features thus derived from different languages are used to train a multilayer feedforward neural network (MLFFNN) classifier for language identification. The effectiveness of the proposed approach is verified using Oregon Graduate Institute (OGI) multi-language telephone speech corpus and National Institute of Science and Technology (NIST) 2003 language identification database.
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