An Ensemble Learning-Based Bangla Phoneme Recognition System Using LPCC-2 Features

  • Himadri Mukherjee
  • Santanu Phadikar
  • Kaushik Roy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)


An array of devices have emerged lately for easing our daily life but one concern has always been towards designing simple user interface (UI) for such devices. A speech-based UI can be a solution to this, considering the fact that it is one of the most spontaneous and natural modes of interaction for most people. The process of identification of words and phrases from voice signals is known as Speech Recognition. Every language encompasses a unique set of atomic sounds termed as Phonemes. It is these sounds which constitute the vocabulary of that language. Speech Recognition in Bangla is a bit complicated task mostly due to the presence of compound characters. In this paper, a Bangla Phoneme Recognition system is proposed to help in the development of a Bangla Speech Recognizer using a new Linear Predictive Cepstral Coefficient-based feature, namely LPCC-2. The system has been tested on a data set of 3710 Bangla Swarabarna (Vowel) Phonemes, and an accuracy of 99.06% has been obtained using Ensemble Learning.


Phoneme LPCC-2 Ensemble Learning Mean Standard deviation 



The authors would like to thank the students of West Bengal State University for voluntarily providing the voice samples during data collection.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Himadri Mukherjee
    • 1
  • Santanu Phadikar
    • 2
  • Kaushik Roy
    • 1
  1. 1.Department of Computer ScienceWest Bengal State UniversityKolkataIndia
  2. 2.Department of Computer Science & EngineeringMaulana Abul Kalam Azad University of TechnologyKolkataIndia

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