Abstract
A speaker-independent word-recognition system has been developed using multiple classification functions for separating 100 spoken words. The speech signal is first analysed and then non-uniformly time-sampled by referring to word-structure tables to construct a word pattern vector of 120 dimensions. Equivalently piece-wise quadratic classification functions are calculated based on a linear-programming algorithm using a large number of spoken-word design samples. A hardware system for real-time recognition has been built as a high-speed microprocessor complex. Using the classification functions calculated from design samples of 100 speakers, a recognition rate of 99% has been obtained for 50 unknown speakers.
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.
