Title: Statistical Parametric Synthesis for Multilingual Speech
       Synthesis
Speaker: Alan W Black, LTI, CMU
        (includes joint work with Kishore Prahallad and joint work
        with Tanja Schultz)

Unit selection synthesis has offered quality speech synthesis merely
at the cost of a large well labeled appropriate speech database.  As
the desire for an easier method for building voices increases
alternative methods are being sought.  HMM-Generation synthesis, as
typified by NITECH's HTS, has been shown to produce high quality
acceptable speech output without the laborious hand correction of
large databases.

This talk presents the FestVox CLUSTERGEN trainer and synthesizer for
automatically building Statistical Parametric Synthesis voices.  In an
effort to generalize HTS in a language independent way we have more
tightly coupled a parametric synthesizer build process into FestVox.
The process is language independent and robust to less perfect and
smaller databases.  The resulting synthesis quality is comparable to
HTS.

In an attempt to investigate multi-lingual synthesis, where cross
language data is used to generate target language synthesizer the talk
will report on a number of multilingual experiments using the
CLUSTERGEN synthesizer, and GlobalPhone multilingual data originally
collected for speech recognition modeling.

CLUSTERGEN is not seen as a replacement to HTS, it is not as elaborate
as the current work in HMM-generation synthesis, but it is seen as a
tighter coupling with FestVox and a framework in which we can carry
out future Statistical Parametric Synthesis work.  Limitations and
intended future improvements will also be discussed.