Abstract
A major difficulty in continuous speech recognition research is the lack of effective and objective evaluation of the statistical models of text. Herbert Simon's view for evaluating theories is here applied to the statistical modelling of text. Three significant contributions can be identified. First, a time‐series representation of text is used to identify three well‐known empirical laws of text generation. These laws provide an effective and objective approach for evaluating four leading statistical models of text. Second, it is shown that the Simon‐Yule model of text provides a constructive mechanism for those laws. Third, based on Simon's explanatory processes of imitation and association, an adaptive framework for continuous speech recognition is suggested.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
