Investigating Khasi Speech Recognition Systems using a Recurrent Neural Network-Based Language Model

Investigating Khasi Speech Recognition Systems using a Recurrent Neural Network-Based Language Model
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© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Fairriky Rynjah, Bronson Syiem, L. Joyprakash Singh
DOI : 10.14445/22315381/IJETT-V70I7P227

How to Cite?

Fairriky Rynjah, Bronson Syiem, L. Joyprakash Singh, "Investigating Khasi Speech Recognition Systems using a Recurrent Neural Network-Based Language Model" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 269-274, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P227

Abstract
The language model (LM) plays a vital role in automatic speech recognition systems (ASRs), and it remains a challenging task, particularly with low/under-resourced languages. Khasi language being an under-resourced language, very little study has been done on the Khasi speech recognition system. To date, no Khasi speech recognition system has been developed using a recurrent neural network-based language model (RNN-LM). This paper presents an investigation of Khasi speech recognition systems using an RNN-LM. In the study, different acoustic models (AMs) are built. The study shows that RNN-LM performs better compared to the traditional N-gram model. Further, using RNN-LM, a reduction of word error rate (WER) in the range of 2.8-3.8% for more speech data and 2.4-3.5% for lesser speech data are observed. In addition, two acoustic features are studied, and from the experimental results, it is found that the Mel frequency cepstral coefficient (MFCC) yields better performance than perceptual linear prediction (PLP). The investigation is performed in the two most widely spoken dialects of the Khasi language.

Keywords
Acoustic model, Deep neural network, Language model, Under-resourced language, Word error rate.

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