TR-EE 89-54: Analyzing NETtalk for Speech Development Modelling

[M Daniel Tom]

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Requests from within US, Canada, and Mexico:

    The technical report with figures, and cluster plots, have been 
placed in the account kindly provided by Ohio State.  Here is the 
instructions to get the files:

        unix> ftp cheops.cis.ohio-state.edu    (or, ftp 128.146.8.62)
        Name: anonymous
        Password: neuron
        ftp> cd pub/neuroprose
        ftp> mget tenorio.*    (type y and hit return)
        ftp> quit
        unix> uncompress tenorio.*.Z
        unix> lpr -P(your_postscript_printer) tenorio.speech_dev.ps
        unix> lpr -P(your_132_column_printer) tenorio.cluster.plain

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Requests from outside North America:

    The technical report is available at a cost of US$8.38 per copy,
postage included.  Please make checks payable to Purdue University
in US dollars.  You may send your requests, checks, and full first
class mail address to:

        J. L. Dixon
        School of Electrical Engineering
        Purdue University
        West Lafayette, Indiana 47907
        USA

Please mention the technical report number: TR-EE 89-54.
Please also note that the hard copy of the technical report does not
include cluster plots mentioned above.

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 Adaptive Networks as a Model for Human Speech Development

                    M. Fernando Tenorio
                       M. Daniel Tom
              School of Electrical Engineering
                            and
                    Richard G. Schwartz
        Department of Audiology and Speech Sciences

         Parallel Distributed Structures Laboratory
                     Purdue University
                 West Lafayette, IN  47907

                        TR-EE 89-54
                        August 1989

Abstract

Unrestricted English text can be converted  to  speech  through
the  use  of a look up table, or through a parallel feedforward
network of deterministic processing units.  Here, we  reproduce
the network structure used in NETtalk.  Several experiments are
carried out to determine which characteristics of  the  network
are  responsible  for  which learning behavior, and how closely
that maps  into  human  speech  development.   The  network  is
trained  with  different  levels of speech complexity (children
and  adult  speech,)  and  with  Spanish  a  second   language.
Developmental  analyses  are  performed  on networks separately
trained  with  children  speech,  adult  speech,  and  Spanish.
Analyses  on second mapping training are performed on a network
trained with Spanish as  a  second  language,  and  on  another
network  trained  with  English  as a second language.  Cluster
analyses of the hidden layer units of networks having different
first  and  second  language  mappings  reveal  that  the final
mapping and  the  convergence  process  depend  a  lot  on  the
training data.  The results are shown to be highly dependent on
statistical characteristics of the input.