5th International Conference on Spoken Language Processing

Sydney, Australia
November 30 - December 4, 1998

Extended Linear Discriminant Analysis (ELDA) for Speech Recognition

Guenther Ruske, Robert Faltlhauser, Thilo Pfau

Institute for Human-Machine-Communication, Technical University of Munich, Germany

Speech recognition systems based on hidden Markov models (HMM) favourably apply a linear discriminant analysis transform (LDA) which yields low-dimensional and uncorrelated feature components. However, since the distributions in the HMM states usually are modeled by mixture gaussian densities, the description by second-order moments no longer is correct. For this purpose we introduced a new "extended linear discriminant analysis" transform (ELDA) which starts from conventional LDA. The ELDA transform is derived by use of a gradient descent optimization procedure based on a "minimum classification error" (MCE) principle, which is applied to the original high-dimensional pattern space. The transform matrix, the best fitting prototype of the correct class (i.e. HMM state) and the nearest rival are adapted. We developed a method which additionally updates all prototypes by a separate maximum likelihood (ML) estimation step. This avoids that such means and covariances, which mostly remain unaffected by the MCE procedure, may diverge step by step.

Full Paper

Bibliographic reference.  Ruske, Guenther / Faltlhauser, Robert / Pfau, Thilo (1998): "Extended linear discriminant analysis (ELDA) for speech recognition", In ICSLP-1998, paper 0100.