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From: kfl@speech2.cs.cmu.edu (Kai-Fu Lee)
Newsgroups: comp.ai,comp.ai.neural-nets
Subject: Re: NETtalk results
Keywords: Neural Networks, NETtalk
Message-ID: <6936@pt.cs.cmu.edu>
Date: 11 Nov 89 16:08:37 GMT
References: <1690@cod.NOSC.MIL> <77404@linus.UUCP> <13659@orstcs.CS.ORST.EDU> <20676@mimsy.umd.edu>
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Organization: Carnegie-Mellon University, CS/RI
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Quoting from Dennis Klatt's "Text-to-speech Conversion" In the Journal
of Acoustical Society of America, Sep. 1987:

"[Sejnowsk's NETALK was trained and tested] .. on a 20,000 word phonemic
dictionary.  When evaluated on the words of this training set, the network
was correct for about 90% of phonemes and stress patterns.

... A typical knowledge-based rule system is calimed to perform at
about ... 97%.

... Lucassen and Mercer ... used the forward-backward algorithm of the IBM
speech recognition strategy on a 50,000 word lexicon...  They obtained
correct letter-to-phoneme correspondences for 94% of the letters in words in
a random sample from a 5000 word office-correspondence task."

Note that study (1) tests on the training set (earlier posts indicates
at most 80% accuracy was obtained on test set), while study (3) reports
on test set results, and study (2) is somewhere in the middle.  So as far
as performance is concerned, NETALK does not work nearly as well as
conventional techniques.