Incorporation of syntax and semantics to improve the performance of an automatic speech recognizer

Abstract

Automatic Speech Recognition (ASR) is a technology that allows a computer to identify spoken words and translate those spoken words into text. Speech recognition systems have started to be used in may application areas such as healthcare, automobile, e-commerce, military, and others. The use of these speech recognition systems is usually limited by their poor performance. In this research we are looking at improving the performance of the baseline ASR systems by incorporating syntactic structures in grammar into an existing Northern Sotho ASR, based on hidden Markov models (HMMs). The syntactic structures will be applied to the vocabulary used within the healthcare application area domain. The Backus Naur Form (BNF) and the Extended Backus Naur Form (EBNF) was used to specify the grammar. The experimental results show the overall improvement to the baseline ASR System and hence give a basis for following this approach.