Title: Investigation on large vocabulary continuous Kannada speech recognition

Authors: Puttaswamy Gowda Vanajakshi; M. Mathivanan; T. Senthil Kumaran

Addresses: ACS College of Engineering, Visveswaraya Technological University, Belgaum, India ' ACS College of Engineering, Visveswaraya Technological University, Belgaum, India ' ACS College of Engineering, Visveswaraya Technological University, Belgaum, India

Abstract: The proposed work carried out to convert the Kannada speech signal to a text document using Sphinx tools. The system is implemented for the large vocabulary of the Kannada speech signal by using the acoustic model (AM) and language model (LM) with the decoder. The AM extracted the Mel frequency cepstral coefficients (MFCC) of the speech signal successfully and trained these coefficients using hidden Markov model (HMM) and tweak the estimation of the AM using Baum-Welch method. The required language model (LM) format is built for the decoder using n-gram count. The decoder is configured to create a text file to the corresponding input speech signal using Sphinx3. The proposed automatic speech recognition (ASR) system achieves a better recognition rate with less word error rate (WER) with AM and LM adaptation using Sphinx3 for large vocabulary and Pocket Sphinx implemented on RasberryPi3 results better accuracy than the other ASR system.

Keywords: acoustic model; Kannada speech; language model; large vocabulary; speech recognition.

DOI: 10.1504/IJBET.2021.115984

International Journal of Biomedical Engineering and Technology, 2021 Vol.36 No.1, pp.1 - 24

Received: 04 May 2020
Accepted: 29 Jun 2020
Published online: 06 Jul 2021 *

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