Effects of Language Modelling for Sepedi-English Code-Switched Speech in Automatic Speech Recognition System

Speech is the primary means of communication among people. Spoken dialogue system give out some means for people to be able to interact with computer systems. The automatic speech recognition system itself is forms part a of spoken dialogue systems. This type of system did a great job for European languages with more challenges encountered for the recognition of South African languages. In this study, we investigate the appropriate approaches for the development of language models for the recognition of Sepedi-English code-switched speech and their effect in ASR. The SRI Language Modeling (SRILM) toolkit was used to develop the Language Model (LM). The Kaldi toolkit to develop ASR system was chosen which is specifically used for speech recognition. This toolkit was used to evaluate the effects of the smoothing techniques. We have evaluated Four smoothing techniques namely Good-Turing (GT), Witten-Bell (WB), Modified Kneser-Ney (MKN), and Laplace (LP) Smoothing. The Witten-Bell smoothing technique was found to perform better than the other three smoothing techniques for Sepedi-English CS data in Language Modelling and also in our ASR.