Abstract
Distributed speech recognition (DSR) where the recognizer is split up into two parts and connected via a transmission channel offers new perspectives for improving the speech recognition performance in mobile environments. In this work, we present the integration of hybrid acoustic models using tied-posteriors in a distributed environment. A comparison with standard Gaussian models is performed on the AURORA2 task and the WSJ0 task. Word-based HMMs and phoneme-based HMMs are trained for distributed and non-distributed recognition using either MFCC or RASTA-PLP features. The results show that hybrid modeling techniques can outperform standard continuous systems on this task. Especially the tied-posteriors approach is shown to be usable for DSR in a very flexible way since the client can be modified without a change at the server site and vice versa.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.