DRAGON SYSTEMS RESOURCE MANAGEMENT BENCHMARK RESULTS FEBRUARY 1991

James Baker; Janet Baker; Pard Bamberg; Larry Gillick; Lori Lamel; Robert Roth; Francesco Scattone; Dean Sturtevant; Ousmane Ba; Richard Benedict

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• Publié par: Odwou
• Nombre de lectures: 29
• Langue: English

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DRAGON SYSTEMS RESOURCE MANAGEMENT
BENCHMARK FEBRUARY
James Janet
Scattone, Dean
Dragon Systems, Inc.
320 Nevada Street
Newton, Massachusetts 02160
[e-mail]
TEL: [phone]
FAX: [phone]
ABSTRACT
Recognition are given for the RM1 In this paper we present preliminary at Dragon Systems on
the Resource benchmark The basic units of data and for Feb91
our system are Phonemes-m-Context (PICs), which are represented as In we make at
Hidden Mmkov Models, each of which is eapressed as sequence of
the of our Elements The PELs to given phoneme
kind of alphabet for the of PICs. to in the and
signal processing algorithm. Our experimentation was
For the tests, two basic methods of the acoustic
performed using the development test models were investigated. 'nac first method of training the Resouro~
Managemera models is to the models for each test speaker from ~ta~ and dam
that speaker's training keeping the PEL of the PICs The in we that we are on
second approach is to use the models from the first melhod to
the 1991 derive segmentation of the then to the PICs in hrgely
speaker-depmdmt manner in order to improve the of speaker tun the only one and
full explanation of these methods is given, as are using the dam not yet
each method.
In to repotting on two we disoass N-
2. OVERVIEW OF THE DRAGON Best The N-Best algorithm is of the
proposed by Soong and Huang at the Jtme This CSR SYSTEM
runs as step and uses an A*-search (an also
known as decoder').
was at the June 1990 DARPA The
is and was be INTRODUCTION
of on an word
In we on some done at reports), when on
on The signal processing is performed by an additional
task. brief of The at kHz and
the is only
the to on -- up
RM are Our to make to kHz and an ~ameter -- of
the in ways 20 ms and as to
Dragon's The HMM-based
so far have the of
and speaker-dependent The The unit used the
in in 4. or PIC, the word
work was by the Agency and was by the and
Command under
59
N000-39-86-C-0307.
1.
12
844
'stack
to arc these Approximately
emulate
sentences
refers
general
labeling
alternatives.
semi-automatic
test
course
preprocessing
information
consonants,
about
speaker
selected
comparable
surrounding
Recognition
phonetic
the
environment
through
were
Dragon's
is
set
turn,
3
which
lexicon
determine
unstressed
the
suggests
acoustic
error
character
speaker's
PICs,
BEF,
sequence
selected
a
using
concatenating
question
question.
that
Several
board.
obtained
from
alternative
return
approaches
indicating
sentence)
a
appeared
model
(or
entries
the
multiple
literature
such
a
words,
Currently,
errl!dated
context
task.
model
finding
recognition
training
models
the
includes
the
available.
comes
identity
phrases,
training
the
relevant
PICs
which
speaker
cases
a
succeeding
signal
phonemes
emulate
rare
highly
well
possible
models
this
whether
signal
speaker's
above,
phoneme
a
reference
lexicon
speaker,
hand.
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phonemes
prepausally
contains
lengthened
vowels
utterances
returns
training
in
are
syllabic
modeled
language
600
will
a
software
sequence
standard
the
stress
entirely
stressed
(phonetic
comparison
elements),
expected
each
39,000
based
that
which
speakers
represents
rates
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recorded.
were
rate
estimates
average
duration
constraint
parameters
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acoustic
pair
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reference
which
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shared
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build
words
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texts
representing
phrases
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training
goal
sentences
phoneme.
hardware,
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using
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Modifications
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score
speaker-
current
build
English
strategy
were
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word.
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candidate
presented
score
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English
Section
24
automatic
probability
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(each
report[2].
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stress),
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3
extend
consonants.
mistakes.
22%
sentence
soon.
hypotheses
available
subject
SNOR
rapid
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pruning
pronunciations
used
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errors
differences,
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proportion
performance
paths.
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Another
explicit
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