Applications of Support Vector Machines to Speech Recognition1

TO SPEECH RECOGNITION1 Aravind Ganapathiraju Jonathan Hamaker and Joseph Picone Conversay Inst. for Signal and Information Processing 15375 NE 90th St. Mississippi State University Redmond, WA, USA Mississippi State, MS 39762, USA [email protected] {hamaker, picone}@isip.msstate.edu ABSTRACT Statistical techniques based on hidden Markov Models (HMMs) with Gaussian emission densities have dominated signal processing and pattern recognition literature for the past 20 years. However, HMMs trained using maximum likelihood techniques suffer from an inability to learn discriminative information and are prone to overfitting and over-parameterization. Recent work in machine learning has focused on models, such as the support vector machine (SVM), that automatically control generalization and parameterization as part of the overall optimization process. In this paper, we show that SVMs provide a significant improvement in performance on a static pattern classification task based on the Deterding vowel data. We also describe an application of SVMs to large vocabulary speech recognition, and demonstrate an improvement in error rate on a continuous alphadigit task (OGI Aphadigits) and a large vocabulary conversational speech task (Switchboard). Issues related to the development and optimization of an SVM/HMM hybrid system are discussed.