Abstract for rosti_icassp2002

In Proc. ICASSP2002, volume 1, pages 949-952.


A-V.I. Rosti & M.J.F. Gales

May 2002

This paper presents a general form of acoustic model for speech recognition. The model is based on an extension to factor analysis where the low dimensional subspace is modelled with a mixture of Gaussians hidden Markov model (HMM) and the observation noise by a Gaussian mixture model. Here the HMM output vectors are the latent variables of a general factor analyser. The model combines shared factor analysis with a dynamic version of independent factor analysis. This factor analysed HMM (FAHMM) provides an alternative, compact, model to handle intra-frame correlation. Furthermore, it allows variable dimension subspaces to be explored. A variety of model configurations and sharing schemes are examined, some of which correspond to standard systems. The training and recognition algorithms for FAHMMs are described and some initial result with Switchboard are presented.

| (ftp:) rosti_icassp2002.pdf | (http:) rosti_icassp2002.pdf | (ftp:) rosti_icassp2002.ps.gz | (http:) rosti_icassp2002.ps.gz |

If you have difficulty viewing files that end '.gz', which are gzip compressed, then you may be able to find tools to uncompress them at the gzip web site.

If you have difficulty viewing files that are in PostScript, (ending '.ps' or '.ps.gz'), then you may be able to find tools to view them at the gsview web site.

We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.