Edinburgh Napier University

  This paper demonstrates the significance of using contextual information in machine learning and speech recognition. While the benefits of contextual information in human communication are widely known, their significance is rarely explored or discussed with a view to their potential for improving speech recognition accuracy. The presented research primarily focuses on an undertaken empirical study that looks at how context affects human communication and understanding. During the study, comparisons between human communication with and without context, have shown overall recognition improvements of over 30% when contextual information is provided. The study has also investigated the importance of the former/middle/latter part of a word towards recognition. These results show that the first two-thirds of a spoken word are key for humans to correctly infer a word. The conclusions from the performed study are then drawn upon to identify useful types of context that can help a machine's understanding, and how such contextual information can be gathered in speech recognition and machine learning systems. This paper shows that context is not only highly important for human communication, but can easily provide a wealth of information to enhance computational systems.