Sixth International Conference on Spoken Language Processing
(ICSLP 2000)

Beijing, China
October 16-20, 2000

Hybrid SVM/HMM Architectures for Speech Recognition

Aravind Ganapathiraju, Jonathan Hamaker, Joseph Picone

Institute for Signal and Information Processing, Department for Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA

In this paper, we describe the use of a powerful machine learning scheme, Support Vector Machines (SVM), within the framework of hidden Markov model (HMM) based speech recognition. The hybrid SVM/HMM system has been developed based on our public domain toolkit. The hybrid system has been evaluated on the OGI Alphadigits corpus and performs at 11.6% WER, as compared to 12.7% with a triphone mixture-Gaussian HMM system, while using only a fifth of the training data used by triphone system. Several important issues that arise out of the nature of SVM classifiers have been addressed. We are in the process of migrating this technology to large vocabulary recognition tasks like SWITCHBOARD.


Full Paper

Bibliographic reference.  Ganapathiraju, Aravind / Hamaker, Jonathan / Picone, Joseph (2000): "Hybrid SVM/HMM architectures for speech recognition", In ICSLP-2000, vol.4, 504-507.