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HMM-Based Lightweight Speech Recognition System for Gujarati Language

  • Jinal H. Tailor
  • Dipti B. Shah
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 10)

Abstract

Speech recognition system (SRS) is growing research interest in the area of natural language processing (NLP). To develop speech recognition system for low resource language is difficult task. This paper defines a lightweight speech recognition system approach for Indian Gujarati language using hidden Markov model (HMM). The aim of this research is to design and implement SRS for routine Gujarati language which is difficult due to language barrier, complex language framework, and morphological variance. To train the HMM-based SRS we have manually created speech corpora that contained 650 routine Gujarati utterances which are recorded from total 40 speakers of South Gujarat region. Total numbers of speakers are selected on the basis of gender. We have achieved accuracy of 87.23% with average error rate 12.7% based on the word error rate (WER) computing.

Keywords

Gujarati language Hidden Markov model Speech recognition Word error rate (WER) 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.S.P. UniversityGujaratIndia
  2. 2.G.H. Patel Post Graduate Department of Computer ScienceS.P. UniversityGujaratIndia

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