Language Identification Based on the Variations in Intonation Using Multi-classifier Systems

  • Shinjini Ghosh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10682)


In this article we make use of the characteristics of tonal languages and machine learning methodologies to understand the patterns in them. Instead of analyzing the absolute pitch or frequency, we analyze how one tone transitions to another in speech. Features (namely, zero crossing count, short time energy, minimum formant frequency, maximum formant frequency) are extracted using the tonal transitions over segments of audio signals. We have developed a multi-classifier system using four classifiers, namely maximum likelihood estimate (MLE), minimum distance classifier (MDC), k-nearest neighbor (kNN) classifier and fuzzy k-NN classifier to automatically identify tonal languages from audio signals. Initially, each individual classifier is trained with existing known data represented by the extracted features. The trained classifier is then used for language identification. Results obtained from these classifiers are combined to generate the final output. Experiments are conducted using three different tonal languages, namely, Chinese, Thai and Vietnamese. The output reveals that the developed multi-classifier model is able to produce promising results. The extracted features produced better results in comparison to usually used frequency value (as a feature). Ensemble of classifiers is a better tool than using individual classifiers.


Tonal language Language identification Classification Multi-classifier 



An earlier version of this work has been presented at the Intel International Science and Engineering Fair (Intel ISEF), held at Los Angeles, USA in May 2017 and won a Grand Award. The author would like to acknowledge her School teacher, Dr. Partha Pratim Roy, for advising her throughout the course of this work. Thanks are due to the Intel Initiative for Research and Innovation in Science (IRIS) Scientific Review Committee and her mentors, for their valuable comments. The author also acknowledges Rahul Roy and Ajoy Mondal, her parents’ students, for helping her in conducting the experiments.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.South Point High SchoolKolkataIndia

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