Abstract
System combination is a promising way to obtain a significant improvement in performance as compared to the conventional form of single system model. In the field of automatic speech recognition (ASR), various approaches have been studied focusing on different aspects of feature extraction and acoustic modelling. These approaches can be combined to utilise their complementary information and to cope with the limitations of individual technique. In this paper we have proposed a novel approach in which three acoustic models based on maximum likelihood, discriminative and margin-based estimation are combined using a technique called as confusion network combination. Further, each acoustic model is associated with a different type of feature extractor to derive observation vectors for training and testing. Experimental results show 2%–5% reduction in error rate for Hindi ASR.
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 callMore From: International Journal of Computational Systems Engineering
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.
