Note: Descriptions are shown in the official language in which they were submitted.
WO 94!20952 ~ ~ PCT/US94/02542
METHOD AND APPARATUS FOR VOICE-INTERACTIVE LANGUAGE
INSTRUCTION
BACKGROUND OF THE INVENTION
This invention relates to speech recognition and
more particularly to the types of such systems based on a
hidden Markov models (HMM) for use in language or speech
instruction.
By way of background, an instructive tutorial on
hidden Markov modeling processes is found in a 1986 paper by
Rabiner et al., "An Introduction to Hidden Markov Models,'°
IEEE ASSP Magazine, Jan. 1986, pp. 4-16.
Various hidden-Markov-model-based speech recognition
systems are known and need not be detailed herein. Such
systems typically use realizations of phonemes which are
statistical models of phonetic segments (including allophones
or, more generically, phones) havir_g parameters that are
estimated from a set of training examples.
Models of words are made by making a network fron
appropriate phone models, a phone being an acoustic
realization of a phoneme, a phoneme being the minimum unit of
speech capable of use in distinguishing words. Recognition
consists of finding the most-likely path through the set of
word models for the input speech signal.
Known hidden Markov model speech recognition systems
are based on a model of speech production as a Markov source.
The speech units being modeled are represented by finite state
machines. Probability distributions are associated with the
transitions leaving each node, specifying the probability of
taking each transition when visiting the node. A probability
distribution over output symbols is associated with each node.
The transition probability distributions implicitly model
duration. The output symbol distributions are typically used
to model speech signal characteristics such as spectra.
The probability distributions for transitions and
output symbols are estimated using labeled examples of speech.
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Recognition consists of determining the path through the
Markov network that has the highest probability of generating
the observed sequence. For continuous speech, this path will
correspond to a sequence of word models. "
Models are known for accounting for out-of-
vocabulary speech, herein called reject phone models but '
sometimes called "filler" models. Such models are described
in Rose et al., "A Hidden Markov rdodel Based Keyword
Recognition System," ~roceedincts of IEEE ICASSP, 1990.
The specific hidden Markov model recognition system
employed in conjunction with the present invention is the
Decipher speech recognizes, which is available from SRI
International of Menlo Park, California. The Decipher system
incorporates probabilistic phonological information, a trainer
capable of training phonetic models with different levels of
context dependence, multiple pronunciations for words, and a
recognizes. The co-inventors have published with others
papers and reports on instructional development peripherally
related to this invention. Each mentions early versions of
question and answer techniques. See, for example, "Automatic
Evaluation and Training in English Pronunciation," Proc. ICSLP
~0, Nov. 1990, Kobe, Japan. "Toward Commercial Applications
of Speaker-Independent Continuous Speech Recognition,"
Proceedinas of Speech Tech 91, (April 23, 1991) New York, New
York. "A Voice Interactive Language Instruction System,"
Proceedin~crs of Eurospeech 91, Genoa, Italy September 25, 1991.
These papers described only what an observer of a
demonstration might experience.
Other language training technologies are known. For
example, U.S. Pat. No. 4,969,194 to Ezawa et al. discloses a
system for simple drilling of a user in pronunciation in a
language. The system has no speech recognition capabilities,
but it appears to have a signal-based feedback mechanism using
a comparator which compares a few acoustic characteristics of
speech and the fundamental frequency of the speech with a
reference set.
U.S. Pat. No. 4,380,438 to Okamoto discloses digital
controller of an analog tape recorder used for recording and
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playing back a user's own speech. There are no recognition
capabilities.
U.S. Patent No. 4,860,360 to Boggs is a system for
evaluating speech in which distortion in a communication
channel is analyzed. There is no alignment or recognition of
the speech signal against any known vocabulary, as the
disclosure relates only to signal analysis and distortion
measure computation.
U.S. Patent No. 4,276,445 to Harbeson describes a
speech analysis system which produces little more than an
analog pitch display. It is not believed to be relevant to
the subject invention.
U.S. Patent No. 4,641,343 to Holland et al.
describes an analog system which extracts formant frequencies
which are fed to a microprocessor for ultimate display to a
user. The only feedback is a graphic presentation of a
signature which is directly computable from the input signal.
There is no element of speech recognition or of any other
high-level processing.
U.S. Patent No. 4,783,803 to Baker et al. discloses
a speech recognition apparatus and technique which includes
means for determining where among frames to look for the start
of speech. The disclosure contains a description of a low-
level acoustically-based endpoint detector which processes
only acoustic parameters, but it does not include higher
level, context-sensitive end-point detection capability.
What is needed is a recognition and feedback system
which can interact with a user in a linguistic context-
sensitive manner to provide tracking of user-reading of a
script in a quasi-conversational manner for instructing a user
in properly-rendered, native-sounding speech.
SUN~iARY OF THE INVENTION
According to the invention, an instruction system is
provided which employs linguistic context-sensitive speech
recognition for instruction and evaluation, particularly
language instruction and language fluency evaluation. The
system can administer a lesson, and particularly a language
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lesson, and evaluate performance in a natural voice-
interactive manner while tolerating strong foreign accents
from a non-native user. The lesson material and instructions
may be presented to the learner in a variety of ways, '
including, but not limited to, video, audio or printed visual
text. As an example, in one language-instruction-specific '
application, an entire conversation and interaction may be
carried out in a target language, i.e., the language of
instruction, while certain instructions may be in a language
familiar to the user.
In connection with preselected visual information,
the system may present aural information to a trainee. The
system prompts the trainee-user to read text aloud during a
reading phase while monitoring selected parameters of speech
based on comparison with a script stored in the system. The
system then asks the user certain questions, presenting a list
of possible responses. The user is then expected to respond
by reciting the appropriate response in the target language.
The system is able to recognize and respond accurately and in
a natural manner to scripted speech, despite poor user
pronunciation, pauses and other disfluencies.
In a specific embodiment, a finite state grammar set
corresponding to the range of word sequence patterns in the
lesson is employed as a constraint on a hidden Markov model
(HI~i) search apparatus in an HI~I speech recognizer which
includes a set of hidden Markov models of target-language
narrations (scripts) produced by native speakers of the target
language.
The invention is preferably based on use of a
linguistic context-sensitive speech recognizer, such as the
Decipher speech recognizer available from SRI International of
Menlo Park, California, although other linguistic context-
sensitive speech recognizers may be used as the underlying
speech recognition engine.
The invention includes a mechanism for pacing a user
through an exercise, such as a reading exercise, and a battery
of multiple-choice questions using an interactive decision
mechanism. The decision mechanism employs at least three
~WO 94/20952 ~ ~ ~ ~ ~ ~ '.~; v pCZ'/US94/02542
levels of error tolerance, thereby simulating a natural level
of patience in human-based interactive instruction.
A mechanism for a reading phase is implemented
through a finite state machine or equivalent having at least
5 four states which recognizes reading errors at any position in
a script and which employs a first set of actions. A related
mechanism for an interactive question phase also is
implemented through another finite state machine having at
least four states, but which recognizes reading errors as well
as incorrect answers while invoking a second set of actions.
As part of the linguistically context-sensitive
speech recognizer, the probabilistic model of speech is
simplified by use of a script for narration, while explicitly
modeling disfluencies comprising at least pauses and out-of-
script utterances.
In conjunction with the interactive reading and
question/answer phases, linguistically-sensitive utterance
endpoint detection is provided for judging termination of a
spoken utterance to simulate human turn-taking in
conversational speech.
A scoring system is provided which is capable of
analyzing speech and reading proficiency, i.e., speed and
error rate, by weighting the proportion of time during correct
reading, the ratio of subject reading speed to nominal native
reading speed, and the proportion of "alt" units (a novel
model for speech) in recognized word stream.
In connection with a DSP device or an equally-
powerful processor, the invention allows for real-time
conversation between the system and the user on the subject of
a specific lesson. The invention may be used conveniently at
a location remote from the system through a telephone network
wherein the user accesses the system by selecting a telephone
number and references from visual or memorized materials for
interaction with the system.
The invention will be better understood by reference
to the following detailed description in connection with the
accompanying drawings.
WO 94/20952 PCT/US94IOZ542
BRIEF DESCRIPTION OF THE DRAWINGS
Fig. 1 is a block diagram of a system according to
the invention.
Fig. 2 is a functional block diagram of recognition '
processes employed with the invention.
Fig. 3 is a functional block diagram of processes '
used in connection with the invention.
Fig. 4A1 is a first portion of a flowchart of a
process of pacing a user through a lesson embedded in an
apparatus implemented in accordance with the invention.
Fig. 4A2 is a second portion of a flowchart of a
process of pacing a user through a lesson embedded in an
apparatus implemented in accordance with the invention.
Fig. 4B is a flowchart of a tracking process
according to the invention.
Fig. 5 is a state diagram of a sentence-level
grammar used in a reading mode according to the invention.
Fig. 6 is a state diagram of a word-level grammar
used in accordance with the invention.
Fig. 7 is a state diagram of a sentence-level
grammar used in an answering mode according to the invention.
Fig. 3 is a state diagram of an "alt" structure used
in the grammars according to the invention.
Fig. 9 is a block diagram of a reading speed
calculator.
Fig. 10 is a block diagram of a reading quality
calculator.
DESCRIPTION OF SPECIFIC EMBODIMENTS
Referring to Fig. 1, there is shown a system block
diagram of an instructional apparatus 10 according to the
invention for instructing a user 12 located close to the
appara~us 10 or for instructing a user 12' located remotely
from the apparatus 10 and communicating via telephone 14. The '
local user 12 may interact with the system through a
microphone 16, receiving instructions and feedback through a
loudspeaker or earphones 18 and a visual monitor (CRT) 20.
The remote user 12' receives prompts through a published or
WO 94120952 ~ ~, ~ ~ ~ ~ ~ PCT/US94/02542
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printed text 22, as from a newspaper advertisement, or may
employ some well-known or memorized text. The remote user's
telephone 14 is coupled through a telephone network 24 through
a multiplexer 26. The local user's microphone 16 is also
coupled to the multiplexer 26. The output of the multiplexer
26 is coupled to a preamplifier 28, through a lowpass filter
30 and ther_ to an analog to digital converter 32, which is
part of a digital signal processing (DSP) subsystem 34 in a
workstation or timesharing computer 36. Output from the DSP
subsystem 34 is provided through a digital to analog converter
(DAC) 38 to either or both an amplifier 40 or the telephone
network 24, which are respectively coupled to the speaker 18
or the telephone 14. The CRT 20 is typically the visual
output device of the workstation 36. A suitable DSP subsystem
is the "Sonitech Spirit 30'° DSP card, and a suitable
workstation is the Sun Microsystems SPARCStation 2 UNIX
workstation.
Referring to Fig. 2 in connection with Fig. 1, the
basic operation of the underlying system is illustrated. The
system is preferably built around a speech recognition system
such as the Decipher system of SRI International. The user 12
addresses the microphone (MIC) 14 in response to a stimulus
such as a visual or auditory prompt. The continuous speech
signal of the microphone 14 is fed through an electronic path
to a "front end" signal processing system 42, which is
contained primarily in the DSP subsystem 34 and subject to
control of the mother workstation 36. The front end signal
processing system 42 performs feature extraction, feeding
acoustic feature parameters to a model searcher 44 which is
built around a hidden Markov Model model set (HMM models) 46.
The model searcher 44 performs a "search°' on the acoustic
features, which are constrained by a finite state grammar to
only a limited and manageable set of choices. Hence,
significant latitude can be granted the user in quality of
pronunciation when compared with the HMM models 46. An
application subsystem 48 in the form of a prepared lesson of
delimited grammar and vocabulary communicates with the model
searcher 44. The application subsystem 48 supplies the finite
WO 94/20952 ~ PCTIUS94/02542
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state grammar to the model searcher 44 against which a search
is performed and the model searcher 44 communicates via
backtracing processes. embedded in the speech recognition
system, such as Decipher, recognition or nonrecognition, as '
well as backtrace-generated information, to the application
subsystem 48, which then interacts with the user 12 according
to the invention.
There are two functional modes to a speech
processing system used in connection with the invention, a
training mode and a recognition mode. The processing is
illustrated in reference to Fig. 3. In a training mode, a
training script 102 is presented to a plurality of persons in
a training population 104, each of which produces a plurality
of speech patterns 106 corresponding to the training script
102. The training script 102 and the speech patterns 106 are
provided as an indexed set to a hidden Markov model trainer
108 to build general FiMM models of target language speech 111.
This needs to bP done only once for a target language, which
typically may employ native speakers and some non-native
speakers to generate general HNa2 models of target language
speech. Then an HMM network model compiler 110, using as
input the general HI~i models and the preselected script 114,
builds a network of speech models 113 specifically for the
preselected script. The network model compiler output is
provided to a hidden Markov model-based speech recognizes 112.
In a recognition mode, a preselected script 114,
which is a functional subset of the training script 102 but
does not necessarily include the words of the preselected
script 102, is presented to a trainee/user 116 or even a
device whose pronunciation is to be evaluated. The speech of
the trainee/user 116 is presumed to be in the form of a speech
pattern 118 corresponding to the preselected script 114. The
preselected script 114 and the single speech pattern 118 are
provided as an indexed set to the hidden Markov model speech
recognizes 112. During each current evaluation period (a
phone-length, word-length, phrase-length or even sentence
length-period of time), words are recognized by the recognizes
112. From the number of words recognized during the
~WO 94/20952 ~ ~ PCT/US94/02542
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evaluation period and prior periods, a recognition score set
120 is calculated, passed on to the application subsystem 48
(Fig. 2) serving as a lesson control unit of the type herein
' described. The score set 120 is a snapshot of the recognition
process as embodied in backtrace-generated information. It is
passed to the application subsystem 48/lesson control unit
which employs a finite state machine embodying the decision
apparatus hereinafter explained. The finite state machine,
among other functions, filters the raw score set information
l0 to identify only good renditions of the scripted lesson.
Specifically, it identifies subsets of the score set upon
which to judge the quality of lesson performance, including
reading spend and reading quality.
Fig. 4A is a flowchart of a process of pacing a user
through a lesson embedded in an apparatus implemented in
accordance with the invention. It is implemented as a finite
state machine (FSM) which is embedded in the application
subsystem 48 which controls the interaction of the user 12 and
the lesson material.
In aperation, reference is directed by the FSM to a
script, which may appear on a CRT screen or produced as
printed material to be read. Starting with a sentence index
of i=1 and a word index j=1 (Step A), a tracking process is
executed (Step B). The FSM tests to determine whether the
user has finished reading the last sentence in the script
(Step C), causing an exit to END if true (Step D). Otherwise
the FSM tests to determine whether the user is pausing as
detected by the tracker and has read good (recognizable) words
from the script since the last tracking operation (Step E).
If true, the FSM responds preferably with an aural or visual
positive rejoinder, e.g., the response "okay" (Step F), and
the FSM recycles to the tracking process (Step B).
If on the other hand, the FSM determines that the
user is not pausing after having read good words since the
last tracking operation, the FSM prompts the user by stating:
"Please read from P(i)." (Step G) The P(i) is the beginning
of the identified location in the script of the phrase
containing or immediately preceding the untracked words. The
WO 94/20952 r~ ~ PCT/US94I02542
tracking process is thereafter invoked again (Step H), this
time at a level of patience wherein the user has effectively
one penalty. The FSM then tests for the completion of the
last sentence, as before, in this new level (Step I), and ends '
5 (Step J) if the script has been completed. Otherwise the FSM
tests to determine whether the user i.;s pausing as detected by
the tracking operation and has read good (recognizable) words
from the script (Step K). If true, the FSM responds with a
preferably an aural or visual positive rejoinder, e.g., the
10 response "okay" (Step L), tests for the beginning of a new
sentence (Step M) and if yes the FSM recycles to the tracking
process (Step B), but if no the FSM recycles to track within
the current sentence (Step H).
If words are not being read correctly as indicated
by the tracking operation (Step K), the FSM tests to determine
whether a new sentence has begun (Step N), in which case the
FSM recycles and prompts the user to read from the beginning
of the sentence (Step G). If this is not the beginning of a
sentence, the FSM states: "No, the sentence is S(i). Please
read from P(i)." (Step P). In other words, the user is
presented with a model of the sentence and prompted to start
at the beginning of the sentence, that is, to try again.
After the prompt, the FSM reinvokes the tracking
procedure (Step Q), then tests to see if the last sentence has
been spoken (Step R), ending if YES (Step S), otherwise
testing to see if the user is pausing after having read good
words from the script (Step T). The FSM issues an "ok" if
true (Step U), tests for a new sentence (Step V), restarting
the tracking (to Step Q) if no, otherwise if a new sentence,
resetting to the highest level of patience with tracking (Step
B). If the FSM is not tracking good words, it checks to see
if a new sentence has started (Step W) and if so, prompts the
user tc start reading from the initialize sentence position
P(i) (to Step G). If it is not a new sentence, the FSM shows
a loss of patience by reciting a phrase such as: 'Ok. That
was a nice try. Now read from the beginning of the next
sentence." (i.e., P(i+1)) (Step Z). The sentence counter
index i is then incremented by one sentence (i+1) (Step AA)
WO 94120952 PCT/US94/02542
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and the word counter index j is reset to 1 (Step AB),
returning to the initial tracking process (to Step B), where
the FSM regains its initial level of patience.
' Fig. 4B is a flow diagram of the tracking process
(Steps B, H, Q) used by the FSM of Fig. 4A. The tracking
process examines one second of input speech (Step AC) using
for example a hidden Markov model of speech patterns
corresponding to the preselected script. The FSM updates the
counters (i & j) to the current position (Step AD) and tests
to determine whether the last sentence has been recited (Step
AE). If yes, the tracking process is exited (Step AF). If the
last sentence is not recognized, the FSM then computes a
pause indicator, which is the number of pause phones
recognized since the previous word (Step AG), which is in
general indicative of the length of a pause. It is then
compared with a pause indicator threshold for the current
position (i,j) and exercise strictness level (Step AH). If
the pause indicator exceeds the threshold, the tracking
process is exited (Step AI). If not, the FSM computes a
reject indicator (Step AJ). The reject indicator, which is in
general indicative of the likelihood that the user is not
producing speech corresponding to the preselected script, is
computed for instance by summming all reject phones returned
by the recognizer since the last word.
The reject indicator is thereafter compared to a
reject indicator threshold (Step AK), which is a function of
the exercise scoring strictness level or of the current
position in the text. If the indicator exceeds the threshold,
the procedure is exited (Step AL). If not, a reject density
is computed (Step AM).
Reject density is computed by examining a previous
. number of scripted words (e.g., five) counting the number of
reject phones returned by the recognizer, and then dividing
the number of reject phones by the sum of the number of reject
phone and the number of scripted words (five). That quotient
is the reject density. Thus, variations in pause lengths do
not impact the reject density.
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The reject density is thereafter compared with a
reject density threshold (a function of exercise strictness
level, text position or both) (Step AN). If the reject
density exceeds the threshold,.the tracking process is ended '
(Step AO); otherwise the tracking process is continued (Step
.t ..
AC ) . ' .
The reject indicator threshold, reject density
threshold and pause indicator threshold may be variably
adjusted as a function of level of strictness or position in
text. The adjusting may be done by the user, by the lesson
designer or automatically by the system.
Referring to Fig. 5, there is shown a structure for
a sentence-level grammar during the reading phase of the
lesson. The sentence level grammar and associated linguistic
structures provide the structural sophistication needed to
accommodate pauses, hesitation noises and other out-of-script
speech phenomenon expected of speech of a student speaker.
The grammar consists of "alt" structures 122 separating
sentences 126, 128, 130 which have been recognized from the
scripted speech patterns. The purpose of the "alt" structure
122 (etc.) is to identify or otherwise account for out-of-
script (nonscripted or unscripted) speech or silence (not
merely pauses) which is likely to be inserted by the reader
into the reading at various points in the reading or answering
exercise. An alt structure according to the invention may be
used in a hidden Markov model-based speech recognition system
to add versatility to a basic speech recognizes enabling it to
handle extraneous or unscripted input in an explicit fashion.
Referring to Fig. 6, there is shown the structure of
a word-level grammar for a sentence, in either the reading
mode or the answering mode. Unlike known word level grammars
where a specific key is sought for detection, this grammar
explicitly anticipates recitation disfluencies between every
word and thus consists of an alt structure 132, 134 between
each ordered word 136, 138, each one leading to the next.
Whereas words may be returned by the recognizes as atomic
units, alt structures are analyzed and returned by the
recognizes as strings of reject phones and pause phones which
~WO 94/20952 ~ ~ PCT/US94/02542
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constitute the alt structures as further detailed herein.
This gives the application subsystem 48 (Fig. 2) the ability
to render higher-level decisions regarding reading by a user.
' Referring to Fig. 7, there is shown the structure of
a sentence-level grammar in the answering mode. An initial
alt 140 is connected by trajectories to any one of a plurality
of answers 142, 144, 146, 148 as alternatives, and each of the
answers is connected by trajectories to a final alt 150. This
grammar for rejecting unanticipated replies from the user by
looping on the initial alt 140, rejecting speech after a valid
answer by looping on the final alt 150 or by accepting
interjections and pauses during the rendition one of the valid
answers.
Fig. 8 illustrates the alt structure 152 common to
all alts. The alt structure 152 is a network of hidden Markov
states, the parameters of which are trained to account for
acoustic features corresponding to out-of-script speech,
silence or background noise. It consists of a "pause'° model
154 and a "reject" model 156 along alternative forward
transition arcs 158, 160, and 162, 164 between an initial node
166 and a terminating node 168. Between the initial node 166
and the terminating node 168 there are also a direct forward
transition arc 170 and a direct return transition arc 172.
The internal structure of the pause model 154 and the reject
model 156 consists of three Markov states and five transition
arcs, which is the exact structure used for models of other
phones in the Decipher speech recognition system available
from SRI International of Menlo Park, California.
The pause model 154 is a phone which is trained on
non-speech segments of the training data (typically recorded)
and comprises primarily examples of silence or background
noise occurring in the training data. The model 156 for the
reject phone is a phone which is trained on a wide variety of
speech which has been selected randomly or periodically from
the training data.
The alt structure 152 with the pause model phone 154
and the reject model phone 156, fully trained, is connected
internally by the transition arcs to allow for all of the
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following possible events: prolonged silence (multiple loops
through the pause phone 154 and the return arc 172); prolonged
out-of-script speech (multiple loops through the reject phone
156 and the return arc 172); alternating periods of silence
and out-of-script speech; and no pause and no out-of-script
speech (bypass on forward transition arc 170). '
The initial transition arcs 158 or 162 leading to
the pause phone 154 and to the reject phone 156 are in one
embodiment of the invention equally weighted with a
probability of 0.5 each.
Referring to Fig. 9, there is shown a reading speed
calculator 180 according to the invention. It receives from
the application subsystem 48 (the finite state machine) a
subset (array of data) 182 of the score set 120 identifying
the elements of good speech by type (words, pause element,
reject element) and position in time, plus certain related
timing. Probability information is available but need not be
used.
Reading speed is extracted by use of a word counter
184, to count the '°good" words, and a timer 186, which
measures or computes the duration of the phrases containing
the filtered (good) words. A reading speed score 190 is
determined from a divider 188 which divides the number of
"good" words W by the time elapsed T in reciting the accepted
phrases containing the "good'° words.
The subsystem herein described could be implemented
by a circuit or by a computer program invoking the following
equations:
Fig. 10 illustrates a mechanism 192 to determining a
reading quality score 230. In connection with the system,
there is a word count source 194 providing a count value 195
for number of words in the preselected script, a mechanism 196
by which the optimum reading time 197 of the script is
reported, a means 198 for counting number of reject phones
(199), a means 200 for measuring total time elapsed 201 during
reading of all words in the preselected script, and a means
202 for measuring "good" time elapsed 203 during reading of
phrases deemed acceptable by said analyzing means.
,rWO 94/20952 ~ ~~' ~ PCT/US94/02542
A divider means 204 is provided for dividing the
total time value 201 by the good time value 203 to obtain a
first quotient 205, and a weighting means 206 (a multiplier)
is providing for weighting the first quotient 205 by a first
5 weighting parameter ("a") to obtain a first score component
208. The sum of three weighting parameters a, b and c is
preferably 1.0 by convention to permit an assignment of
relative weight of each of three types of quality
measurements.
10 A selector means 210 is provided for selecting a
maximum between the optimum reading time 197 and the good time
203 to produce a preferred maximum value 211. This is used in
valuing a preference between a fast reading and a reading
which is paced according to a preference. In connection with
15 the preference evaluation, a divider means 212 is provided for
dividing the preferred maximum value 211 by the optimum
reading time 197 to obtain a second quotient 213. The second
quotient is weighted by a second weighting parameter (b) by a
weighting means 214 (a multiplier) to obtain a second score
component 216.
An adder or summing means 218 is provided for
summing the number of reject phones 199 and the number of
script words 195 to obtain a quality value 219. A divider
means 220 is provided for dividing the number of words 195 by
the quality value 219 to obtain a third quotient 221. The
third quotient is weighted by a weighting means 222 (a
multiplier) by third weighting parameter (c) to obtain a third
score component 224.
A three-input summing means 226 is provided for
summing the first, second and third score components 208, 216
and 224 to produce a score sum 227. The score sum 227 is
scaled to a percentage or other scale by a weighting means
multiplying by a scale factor 228, such as the value 10 to
obtain the reading quality score 230.
The reading quality evaluation subsystem herein
described cou7.d be implemented by a circuit or by a computer
program invoking the following equation:
RQS = 10 * (a*Tg/Tt + b*(Tn/[max (Tn, Tg)]) + c*W/(Rg + W)
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where:
RQS is the reading quality score on a scale of 1 to 10
(based on the scale factor, herein 10);
a, b, and c are scale factors whose sum equals 1 and in a '
specific embodiment, a=0.25, b=0.25 and c=0.5;
W is the number of words in the text;
Tg is the "good" time or time spent reading good
sentences;
Tt is the total reading time spent reading, excluding
initial and final pauses;
Tn is the optimal reading time, i.e., reading time by a
good native speaker;
Rg is the number of rejects detected during the "good"
renditions of the sentences, i.e., during Tg.
Appendix A is a microfiche appendix of source code
listing of a system according to the invention implemented on
a computer workstation. The language of the source code is C.
The invention has now been explained with reference
to specific embodiments. Other embodiments will be apparent
to those of ordinary skill in this art upon reference to the
present disclosure. It is therefore not intended that this
invention be limited, except as indicated by the appended
claims.
..
