Abstract
The paper presents a Segment-Mean method for reducing the dimension of the speech feature parameters. K-Means function is used to group the speech feature parameters whose dimension has been reduced. And then the speech samples are classified into different clusters according to their features. It proposes a cross-group training algorithm for the speech feature parameters clustering which improves the accuracy of the clustering function. When recognizing speech, the system uses a cross-group HMM models algorithm to match patterns which reduces the calculation by more than 50% and without reducing the recognition rate of the small vocabulary speech recognition system.
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.
