LEARNING NEW ARTICULATOR TRAJECTORIES FOR A SPEECH PRODUCTION MODEL USING ARTIFICIAL NEURAL NETWORKS
C.S. Blackburn and S.J. Young
August 1995
We present a novel method for generating additional pseudo-articulator trajectories suitable for use within the framework of a stochastically trained speech production system recently developed at CUED. The system is initialised by inverting a codebook of (articulator, spectral vector) pairs, and the target positions for a set of pseudo-articulators and the mapping from these to speech spectral vectors are then jointly optimised using linearised Kalman filtering and an assembly of neural networks. A separate network is then used to hypothesise a new articulator trajectory as a function of the existing articulators and the output error of the system. The techniques used to initialise and train the system are described, and preliminary results for the generation of new pseudo-articulatory ainputs are presented.
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.