Acoustic Modeling Using Continuous Density Hidden Markov Models in the Mercer Kernel Feature Space

  • Authors:

  • R. Anitha;C. Chandra Sekhar

  • Affiliations:

  • Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India;Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
  • Year:
  • 2007

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Abstract

In this paper, we propose an approach for acoustic modeling using Hidden Markov Models (HMMs) in the Mercer kernel feature space. Acoustic modeling of subword units of speech involves classification of varying length sequences of speech parametric vectors belonging to confusable classes. Nonlinear transformation of the space of parametric vectors into a higher dimensional space using Mercer kernels is expected to lead to better separability of confusable classes. We study the performance of continuous density HMMs trained using the varying length sequences of feature vectors obtained from the kernel based transformation of parametric vectors. Effectiveness of the proposed approach to acoustic modeling is demonstrated for recognition of spoken letters in E-set of English alphabet, and for recognition of consonant-vowel type subword units in continuous speech of three Indian languages.