Abstract
Investigates the use of discriminative training techniques for large vocabulary speech recognition with training datasets up to 265 hours. Techniques for improving lattice-based maximum mutual information estimation (MMIE) training are described and compared to frame discrimination (FD). An objective function which is an interpolation of MMIE and standard maximum likelihood estimation (MLE) is also discussed. Experimental results on both the Switchboard and North American Business News tasks show that MMIE training can yield significant performance improvements over standard MLE even for the most complex speech recognition problems with very large training sets.
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.
