An assessment of automatic speech processing technology is presented. Fundamental problems in the development and the deployment of automatic speech processing systems are defined and a technology forecast for speech systems is presented

Doddington, George R.

English

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N93-72627
SPEECH SYSTEMS RESEARCH AT TEXAS INSTRUMENTS
DR. GEORGE R. DODDINGTON
TEXAS INSTRUMENTS INCORPORATED
DALLAS, TEXAS / *y?/ -?fT~/;
I. TI Capabilities
Texas Instruments supports a Speech Systems Research
branch in its Central Research Laboratories. The charter of this
branch is to foster the development of new TI business opportunities
through development and application of automatic speech processing
technology. Seven speech research programs are currently active:
Two corporate funded programs determine the strategic direction of
our speech research; these are programs to develop automatic dicta-
tion technology and low-cost vocoder technology. Three programs are
externally funded by Rome Air Development Center to develop and apply
advanced but near-term speech processing technology. These programs
are: "total voice" speaker verification, limited vocabulary continu-
ous word recognition, and automatic language identification. Finally,
two programs are supported internally by TI's operating divisions.
The Speech Systems Research Computer Laboratory contains a
variety of computer systems for speech research, system evaluation
and product development. System 1, the principal research system,
is diagrammed in Figure 1. The salient features of this system in-
clude real-time speech I/O, a 500 Mbit disk, a Floating Point Systems
AP120B array processor, and a Tektronix interactive graphics terminal
with hard-copy.
II. TI Achievements
A. Voice Authentication
Although Texas Instruments has been active in a
variety of speech processing problems including speech analysis,
speech synthesis, word recognition and speaker verification, most of
our research effort in the past has been devoted to the development
of speaker verification technology. Speaker verification, in its
operational format, we refer to as voice authentication. A sequence
of 3 programs, funded by RADC and beginning in 1972, led to the de-
velopment of a voice authentication technology capable of meeting
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BISS1 performance requirements of less than 1% user rejection .at less than
2% impostor acceptance. In retrospect, there were 3 primary problems that
we solved in this development:
1) Enrollment
Enrollment of a user on the voice authentication
system must be performed in a single session.
Enrollment is difficult for the following reason:
Speech data collected in a single session is rela-
tively self-consistent but not representative of
an ensemble average over many sessions. Therefore,
the initial reference data is biased and the
initial estimate of speaker variance is invalid.
This problem has been effectively solved by
requiring extra speech input data in a special
4-session "post enrollment" strategy and, recently,
an additional special 8-session "post-Post enroll-
ment" strategy.
2) "Goats"
Voice authentication system users are classified
as either "sheep" or "goats". The sheep are well
behaved and far outnumber the goats. The system
performs well on sheep. The speech data of goats
exhibit high variance, but the voice authentica-
tion system must perform well for everyone. Uni-
form performance is achieved by a carefully designed
decision function which requires more speech data
from the goats while, at the same time, not pre-
judicing the verification decision against them.
3) Discipline
Voice authentication system users have little
interest and little interaction with the authenti-
cation system. Authentication utterances often
have false starts or are imbedded in extraneous
speech data. The verification system must be able
to extract the proper input data and discriminate
between proper data and garbage. Time registration
and Installation Security System, a program administered by the
Air Force to define and develop future military security systems.
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through energy end-points cannot solve this prob-
lem. Proper time registration is achieved through
a continuous spectral matching algorithm.
Texas Instruments has controlled access to its Corporate Com-
puter Center by voice authentication for the past three years. This
system is in operation 24 hours/day and has provided over h million
verifications. Current system performance is **% user rejection at an
impostor acceptance level of I3j%. Most user rejections are attributable
to noncooperative quirks of the user.
B. Word Recognition
Texas Instruments has operational real-time demonstra-
tions of word recognition for isolated and connected words, enrolled
and independent speakers, and small vocabularies up to 50 words. Large
vocabulary recognition has been performed in non real time for vocabu-
laries of greater than 1,000 words. Also, the development of automatic
language identification has been sponsored by RADC. One significant
result in language identification is the demonstration of improved
identification by normalization of long-term spectral averages. Although
long-term spectral averages have been shown to be useful in discriminating
languages, the wide variety of recording conditions encountered in our
data base invalidate such utility. With the incorporation of spectral
normalization, results of 5-language identification task have been
improved to 80% correct on excerpts of two minutes duration.
"Total Voice" speaker verification involves two speech
processing tasks: speaker independent recognition of an identifying
sequence of six spoken digits; and speaker verification using the user
identification and the same identifying speech input data. The appli-
cation requires speaker independent recognition of a connected sequence
of six digits with less than 1% sequence recognition error. These
severe application requirements have been achieved by incorporating two
check digits in the sequence for improved recognition accuracy and by
"forbidding" certain digit pairs such as "three-eight".
III. Fundamental Problems
I have ordered below four classes of problems that must
be faced in the development and deployment of an automatic speech pro-
cessing system:
A. Speech Science
The lack of speech science limits the capabilities
of speech processing systems. So, how do we go about getting speech
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science? Careful direction is exceedingly important because one can
easily drown in the vast, uncharted oceans of speech phenomena. In my
opinion, a very good way to get speech science is to identify an impor-
tant real application for speech processing and to persevere in develop-
ing speech technology for this application. This approach, which I
refer to as the "correct" approach, is contrasted with the traditionally
popular method in Figure 2.
B. Cost
Cost is a very important consideration in automatic
speech processing for two reasons: First, speech processing is a complex
problem which is inherently costly, at least in terms of computing power.
Second, system cost effectiveness is usually measured in terms of the
efficiency of a person, at least in the case of speech recognition.
Cost/benefit tradeoffs must be carefully made between speech and other
alternate media.
C. Performance Forecast
It would be nice to do an experiment in the laboratory
and be able to say with confidence that the laboratory results will be
realized in the operational system. This rarely, if ever, happens
because the laboratory data is not representative of operational data.
Sometimes the discrepancy between laboratory results and operational
results is embarrassingly large. One important reason contributing to
this is the typical favorable mix of sheep with goats in laboratory
experiments. (Speech researchers are usually "super sheep".) System
performance depends very strongly on this mix. A large number of sub-
jects is required to properly evaluate system performance. Figure 3
is included to provide some perspective on the sheep/goat problem.
This figure shows a histogram of user data variance for an operational
voice authentication system.
How much data must be included in a laboratory experiment to
provide a good performance forecast? My rule of thumb answer to this
question is that enough data must be collected to provide 30 errors.
Assuming that each trial is independent, thirty errors will provide you
with an estimate of the true error rate within ±30%, for the given data
context, with 90% confidence. Suppose for example that you anticipate
1% error for a certain word recognition system. This implies that at
least 3,000 spoken words must be collected to provide the desired confi-
dence interval on error rate. Note however, that the trials must be
statistically independent. Will you collect 3,000 words from one
speaker or one word from each of 3,000 speakers?
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The Traditional Approach
receive divine inspiration
implement system
collect data
• compile results
• claim fame
The "Correct" Approach
. learn speech science
• improve system
• collect data
• compile results
• analyze errors
Figure 2. Basic Steps in Developing Speech Technology
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D. The Human Factor
User acceptance is a critical factor in speech pro-
cessing systems. For word recognition this includes not only recogni-
tion performance, but also other recognition characteristics. For cur-
rent technology, isolated word recognition machines, a most important
operating characteristic is the requirement for pausing between words.
This is a nontrivial skill to perform reliably and is a major underly-
ing factor in initial performance degradation. Fortunately, system
performance is often aided through the adaptation of the user to the
system. This includes learning to speak clearly and loudly to the sys-
tem in spite of the fact that the microphone is often less than 1 inch
from the mouth. Loud, clear input stabilizes the speech data and
improves system performance. Such user adaptation is clearly demon-
strated in Figure 4. Figure 4 is a plot of user data variance as a
function of session number for an operational voice authentication
system. The subtle feedback in this system has provided a user learn-
ing time constant of 2,000 sessions.
IV. Technology Forecast
Progress in speech systems development is tied closely to
developments in computer technology. Advanced speech system capabilities
will require inexpensive yet highly competent high speed data processing.
The speech processing system will comprise a general purpose CPU with
speech input through a special purpose speech preprocessor and feature
extractor. An important cost element is this speech preprocessor unit.
TI is currently developing, under contract with AKPA, a one-chip speech
analyzer using CCD technology. This chip implements a 19-channel sampled
data filter bank with on-chip 4-bit A/D conversion and multiplexing.
Low cost speech preprocessor technology coupled with advances
in microprocessor performance is anticipated to have substantial impact
on speech system competence, cost and market size by 1980-1982. At
this time useful and affordable capabilities will be introduced for
connected word recognition and narrowband-digital voice communication.
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BIOGRAPHICAL SKETCH
George R. Doddington
Ph.D. in Electrical Engineering, University of Wisconsin
M.S. in Electrical Engineering, University of Wisconsin
B.S. in Electrical Engineering, University of Florida
Professional Engineer, State of Wisconsin
At'Texas Instruments, Dr. Doddington has directed programs of
speech research encompassing advanced speech processing techniques.
This work has included interactive simulations of word recognition,
speech segmentation and analysis, and speaker verification. Dr.
Doddington's doctoral study emphasized communication theory, control
theory, probability theory, and neurophysiology. His doctoral research
was conducted at Bell Telephone Laboratories during 1969 and 1970.
This work comprised the development of a method of nonlinear time normali-
zation of speech and the implementation of this method in a system of
speaker verification. Dr. Doddington joined Texas Instruments in 1970.
Dr. Doddington1s master's thesis comprises a generalized theory for
relating the gross operating characteristics of chromatographic systems
to the statistics of molecular behavior. Dr. Doddington was employed
at the Federal Communications Commission from 1960 through 1963, during
which time he designed and developed a secrecy coding system for radio-
teletype communication. In 1964 he received the bachelor's degree with
high honors for his thesis comprising the theory and practical imple-
mentation of a method of linear amplification approaching 100 percent
efficiency.
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DISCUSSION
Dr. George Doddington
Q: Mark Medress, Sperry Univac; Do you have any feeling for how much
action you got out of the check digits in your total voice verifi-
cation system?
A: Okay, I didn't talk about the performance of that system, really,
except I did say we had eight errors in about a thousand trials.
Yes, I have some feeling for that and I'll tell you. The eight
errors in a thousand trials represented about one percent error.
Now that's on the six digit sequences themselves. We have run some
experiments using the digit recognizer component, in the sequence
recognition strategy, and we've gotten about 95 percent correct
digit recognition. Now those two performance figures are not
comparable. The digit recognition is for digits, 95 percent cor-
rect, and the sequence recognition is for sequences, and that's
about 1 percent error.
Q: Mark Medress; That's 95 percent of the words in those sequences
are correct, is that what you're saying?
A: That's right. 95 percent of all digits were correct.
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