Ninth International Conference on Spoken Language Processing

Pittsburgh, PA, USA
September 17-21, 2006

SPAM and Full Covariance for Speech Recognition

Daniel Povey

IBM T.J. Watson Research Center, USA

The Subspace Precision and Mean model (SPAM) is a way of representing Gaussian precision and mean values in a reduced dimension. This paper presents some large vocabulary experiments with SPAM and introduces an efficient way to optimize the SPAM basis. We present experiments comparing SPAM, diagonal covariance and full covariance models on a large vocabulary task. We also give explicit formulae for an implementation of SPAM.

Full Paper

Bibliographic reference.  Povey, Daniel (2006): "SPAM and full covariance for speech recognition", In INTERSPEECH-2006, paper 2047-Wed1BuP.3.