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Speech recognition

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D.B. Paul

.Speech Recognition Using
Hidden Markov Models
The Lincoln robust hidden Markov model speech recognizer currently provides stateof-the-art performance for both speaker-dependent and speaker-independent largevocabulary continuous-speech recognition. An early isolated-word version similarly
improved the state of the art on a speaker-stress-robustness isolated-word task. This
article combines hidden Markov model and speech recognition tutorials with a
description of the above recognition systems.

1. Introduction
There are two related speech tasks: speech
understanding and speech recognition. Speech
understanding is getting the meaning of an
utterance such that one can respond properly
whether or not one has correctly recognized all
of the words. Speech recognition is simplytranscribing the speech without necessarily knowing the meaning ofthe utterance. The two can be
combined, but the task described here is purely
recognition.
Automatic speech recognition' and understanding have a number of practical uses. Data
input to a machine is the generic use, but in
what circumstances is speech the preferred or
only mode? An eyes-and-hands-busy usersuch as a quality control inspector, inventory
taker, cartographer, radiologist (medical X-ray
reader), mail sorter, or aircraft pilot-is one
example. Another use is transcription in the
business environment where it may be faster to
remove the distraction of typing for the nontypist. The technology is also helpful to handicapped persons who might otherwise require
helpers to control their environments.
Automatic speech recognition has a long
history of being a difficult problem-the first
papers date from about 1950 [1]. DUring this
period, a number of techniques, such as
linear-time-scaled word-template matching,
dynamic-time-warped word-template matching, lingUistically motivated approaches (fmd
the phonemes, assemble into words, as-

The Liru:oln Laboratory Journal, Volume 3, Number 1 (l990)

semble into sentences), and hidden Markov
models (HMM), were used. Of all of the available techniques, HMMs are currently yielding
the best performance.
This article will first describe HMMs and their
training and recognition algorithms. It will then
discuss the speech recognition problem and
howHMMs are used to perform speech recognition. Next, it will present the speaker-stress
problem and our stress-resistant isolated-word
recognition (IWR) system. Finally, it will show
how we adapted the IWR system to large-vocabulary...
D.B.
Paul
.
Speech
Recognition
Using
Hidden
Markov
Models
The
Lincoln
robust
hidden
Markov
model
speech
recognizer
currently
provides
state-
of-the-art
performance
for
both
speaker-dependent
and
speaker-independent
large-
vocabulary
continuous-speech
recognition.
An
early
isolated-word
version
similarly
improved
the
state
of
the
art
on
a
speaker-stress-robustness
isolated-word
task.
This
article
combines
hidden
Markov
model
and
speech
recognition
tutorials
with
a
description
of
the
above
recognition
systems.
1.
Introduction
There
are
two
related
speech
tasks:
speech
understanding
and
speech
recognition.
Speech
understanding
is
getting
the
meaning
of
an
utterance
such
that
one
can
respond
properly
whether
or
not
one
has
correctly
recognized
all
of
the
words.
Speech
recognition
is
simplytran-
scribing
the
speech
without
necessarily
know-
ing
the
meaning
of
the
utterance.
The
two
can
be
combined,
but
the
task
described
here
is
purely
recognition.
Automatic
speech
recognition'
and
under-
standing
have
a
number
of
practical
uses.
Data
input
to
a
machine
is
the
generic
use,
but
in
what
circumstances
is
speech
the
preferred
or
only
mode?
An
eyes-and-hands-busy
user-
such
as
a
quality
control
inspector,
inventory
taker,
cartographer,
radiologist
(medical
X-ray
reader),
mail
sorter,
or
aircraft
pilot-is
one
example.
Another
use
is
transcription
in
the
business
environment
where
it
may
be
faster
to
remove
the
distraction
of
typing
for
the
nontyp-
ist.
The
technology
is
also
helpful
to
handi-
capped
persons
who
might
otherwise
require
helpers
to
control
their
environments.
Automatic
speech
recognition
has
a
long
history
of
being
a difficult
problem-the
first
papers
date
from
about
1950
[1]. DUring
this
period,
a
number
of
techniques,
such
as
linear-
time-scaled
word-template
matching,
dynamic-time-warped
word-template
match-
ing, lingUistically
motivated
approaches
(fmd
the
phonemes,
assemble
into
words,
as-
The Liru:oln
Laboratory
Journal,
Volume
3,
Number
1
(l990)
semble
into
sentences),
and
hidden
Markov
models
(HMM),
were
used.
Of
all
of
the
avail-
able
techniques,
HMMs
are
currently
yielding
the
best
performance.
This
article
will
first
describe
HMMs
and
their
training
and
recognition
algorithms.
It
will
then
discuss
the
speech
recognition
problem
and
howHMMs
are
used
to
perform
speech
recogni-
tion. Next,
it
will
present
the
speaker-stress
problem
and
our
stress-resistant
isolated-word
recognition
(IWR)
system.
Finally,
it
will
show
how
we
adapted
the
IWR
system
to
large-vo-
cabulary
continuous-speech
recognition
(CSR).
2.
The
Hidden
Markov
Model
Template
comparison
methods
of
speech
recognition
(e.g.,
dynamic
time
warping
[2])
directly
compare
the
unknown
utterance
to
known
examples.
Instead
HMM
creates
sto-
chastic
models
from
known
utterances
and
compares
the
probability
that
the
unknown
utterance
was
generated
by
each
model. HMMs
are
a
broad
class
of
doubly
stochastic
models
for
nonstationary
signals
that
can
be
inserted
into
other
stochastic
models
to
incorporate
informa-
tion
from
several
hierarchical
knowledge
sources.
Since
we
do
not
know
how
to
choose
the
form
of
this
model
automatically
but,
once
given a form,
have
efficient
automatic
methods
of
estimating
its
parameters,
we
must
instead
choose
the
form
according
to
our
knowledge
of
the
application
domain
and
train
the
parame-
ters
from
known
data.
Thus
the
modeling
prob-
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