Predicting User Reactions to System Error
Diane Litman and Julia Hirschberg
AT&T Labs-Research
Florham Park, NJ, 07932 USA
a0 diane/julia
a1 @research.att.com
Marc Swerts
IPO, Eindhoven, The Netherlands,
and CNTS, Antwerp, Belgium
m.g.j.swerts@tue.nl
Abstract
This paper focuses on the analysis and
prediction of so-called aware sites,
defined as turns where a user of a
spoken dialogue system first becomes
aware that the system has made a
speech recognition error. We describe
statistical comparisons of features of
these aware sites in a train timetable
spoken dialogue corpus, which re-
veal significant prosodic differences
between such turns, compared with
turns that ‘correct’ speech recogni-
tion errors as well as with ‘normal’
turns that are neither aware sites nor
corrections. We then present machine
learning results in which we show how
prosodic features in combination with
other automatically available features
can predict whether or not a user turn
was a normal turn, a correction, and/or
an aware site.
1 Introduction
This paper describes new results in our continu-
ing investigation of prosodic information as a po-
tential resource for error recovery in interactions
between a user and a spoken dialogue system. In
human-human interaction, dialogue partners ap-
ply sophisticated strategies to detect and correct
communication failures so that errors of recog-
nition and understanding rarely lead to a com-
plete breakdown of the interaction (Clark and
Wilkes-Gibbs, 1986). In particular, various stud-
ies have shown that prosody is an important cue
in avoiding such breakdown, e.g. (Shimojima et
al., 1999). Human-machine interactions between
a user and a spoken dialogue system (SDS) ex-
hibit more frequent communication breakdowns,
due mainly to errors in the Automatic Speech Re-
cognition (ASR) component of these systems. In
such interactions, however, there is also evidence
showing prosodic information may be used as a
resource for error recovery. In previous work,
we identified new procedures to detect recogni-
tion errors. In particular, we found that pros-
odic features, in combination with other inform-
ation already available to the recognizer, can dis-
tinguish user turns that are misrecognized by the
system far better than traditional methods used in
ASR rejection (Litman et al., 2000; Hirschberg et
al., 2000). We also found that user corrections
of system misrecognitions exhibit certain typical
prosodic features, which can be used to identify
such turns (Swerts et al., 2000; Hirschberg et al.,
2001). These findings are consistent with previ-
ous research showing that corrections tend to be
hyperarticulated — higher, louder, longer ...than
other turns (Wade et al., 1992; Oviatt et al., 1996;
Levow, 1998; Bell and Gustafson, 1999).
In the current study, we focus on another turn
category that is potentially useful in error hand-
ling. In particular, we examine what we term
aware sites — turns where a user, while interact-
ing with a machine, first becomes aware that the
system has misrecognized a previous user turn.
Note that such aware sites may or may not also be
corrections (another type of post-misrecognition
turn), since a user may not immediately provide
correcting information. We will refer to turns
that are both aware sites and corrections as corr-
awares, to turns that are only corrections as corrs,
to turns that are only aware sites as awares, and to
turns that are neither aware sites nor corrections as
norm.
We believe that it would be useful for the
dialogue manager in an SDS to be able to de-
tect aware sites for several reasons. First, if
aware sites are detectable, they can function as
backward-looking error-signaling devices, mak-
ing it clear to the system that something has gone
wrong in the preceding context, so that, for ex-
ample, the system can reprompt for information.
In this way, they are similar to what others have
termed ‘go-back’ signals (Krahmer et al., 1999).
Second, aware sites can be used as forward-
looking signals, indicating upcoming corrections
or more drastic changes in user behavior, such
as complete restarts of the task. Given that, in
current systems, both corrections and restarts of-
ten lead to recognition error (Swerts et al., 2000),
aware sites may be useful in preparing systems to
deal with such problems.
In this paper, we investigate whether aware
sites share acoustic properties that set them apart
from normal turns, from corrections, and from
turns which are both aware sites and corrections.
We also want to test whether these different turn
categories can be distinguished automatically, via
their prosodic features or from other features
known to or automatically detectible by a spoken
dialogue system. Our domain is the TOOT spoken
dialogue corpus, which we describe in Section 2.
In Section 3, we present some descriptive findings
on different turn categories in TOOT. Section 4
presents results of our machine learning experi-
ments on distinguishing the different turn classes.
In Section 5 we summarize our conclusions.
2 Data
The TOOT corpus was collected using an experi-
mental SDS developed for the purpose of compar-
ing differences in dialogue strategy. It provides
access to train information over the phone and
is implemented using an internal platform com-
bining ASR, text-to-speech, a phone interface,
and modules for specifying a finite-state dialogue
manager, and application functions. Subjects per-
formed four tasks with versions of TOOT, which
varied confirmation type and locus of initiative
(system initiative with explicit system confirma-
tion, user initiative with no system confirmation
until the end of the task, mixed initiative with im-
plicit system confirmation), as well as whether
the user could change versions at will using voice
commands. Subjects were 39 students, 20 nat-
ive speakers of standard American English and
19 non-native speakers; 16 subjects were female
and 23 male. The exchanges were recorded and
the system and user behavior logged automatic-
ally. Dialogues were manually transcribed and
user turns automatically compared to the corres-
ponding ASR (one-best) recognized string to pro-
duce a word accuracy score (WA) for each turn.
Each turn’s concept accuracy (CA) was labeled
by the experimenters from the dialogue recordings
and the system log; if the recognizer correctly cap-
tured all the task-related information given in the
user’s original input (e.g. date, time, departure or
arrival cities), the turn was given a CA score of
1, indicating a semantically correct recognition.
Otherwise, the CA score reflected the percentage
of correctly recognized task concepts in the turn.
For the study described below, we examined 2328
user turns from 152 dialogues generated during
these experiments. 194 of the 2320 turns were re-
jected by the system.
To identify the different turn categories in the
corpus, two authors independently labeled each
turn as to whether or not it constituted a correction
of a prior system failure (a CA error or a rejection)
and what turn was being corrected, and whether
or not it represented an aware site for a prior fail-
ure, and, if so, the turn which the system had failed
on. Labeler disagreement was subsequently re-
solved by consensus. The fragment in Figure 1,
produced with a version of TOOT in which the
user has the initiative with no confirmation until
the end of the task, illustrates these labels. This
example illustrates cases of corraware, in which
both the user’s awareness and correction of a mis-
recognition occur in the same turn (e.g. turns
1159 and 1160, after system prompts for informa-
tion already given in turn 1158). It also illustrates
cases in which aware sites and corrections occur
in different turns. For example, after the immedi-
ate explicit system confirmation of turn 1162, the
user first becomes aware of the system errors (turn
1163), then separately corrects them (turn 1164);
turn 1163 is thus an aware turn and turn 1164 a
corr. When no immediate confirmation of an ut-
terance occurs (as with turn 1158), it may take sev-
eral turns before the user becomes aware of any
Turn Turn ID Aware of Corr of Type
S: How may I help you?
U: Get me the train to New York City from
Baltimore on Sunday at eight thirty pm 1158 NA NA NORM
S: Which city do you want to go to?
U: New York City 1159 1158 1158 CORRAWARE
S: Which city do you want to leave from?
U: Baltimore 1160 1158 1158 CORRAWARE
S: Do you want me to find the trains from
Baltimore to New York City today at anytime now?
U: No 1161 1158 NA AWARE
S: How may I help you?
U: Get me the train to New York City from
Baltimore on Sunday at eight thirty pm 1162 NA 1158 CORR
S: Do you want me to find the trains from
Baltimore to New York City today at anytime now?
U: No 1163 1162 NA AWARE
S: How may I help you?
U: Get me the train on Sunday at eight thirty pm 1164 NA 1162 CORR
Figure 1: Dialogue Fragment with Aware and Correction Labels.
misrecognition errors. For example, it is not un-
til turn 1161 that the user first becomes aware of
the error in date and time from 1158; the user then
corrects the error in 1162. So, 1161 is classified as
an aware and 1162 as a corr. Note that corr turns
represent 13% of the turns in our corpus, awares
represent 14%, corrawares account for 16%, and
norm turns represent 57% of the turns in the cor-
pus.
3 Descriptive Analysis and Results
We examined prosodic features for each user turn
which had previously been shown to be useful for
predicting misrecognized turns and corrections:a2
maximum and mean fundamental frequency val-
ues (F0 Max, F0 Mean), maximum and mean en-
ergy values (RMS Max, RMS Mean), total dur-
ation (Dur), length of pause preceding the turn
(Ppau), speaking rate (Tempo) and amount of si-
lence within the turn (%Sil). F0 and RMS val-
ues, representing measures of pitch excursion and
loudness, were calculated from the output of En-
tropic Research Laboratory’s pitch tracker, get f0,
with no post-correction. Timing variation was
represented by four features. Duration within and
length of pause between turns was computed from
the temporal labels associated with each turn’s be-
a3 While the features were automatically computed, begin-
nings and endings were hand segmented from recordings of
the entire dialogue, as the turn-level speech files used as in-
put in the original recognition process created by TOOT were
unavailable.
ginning and end. Speaking rate was approximated
in terms of syllables in the recognized string per
second, while %Sil was defined as the percentage
of zero frames in the turn, i.e., roughly the per-
centage of time within the turn that the speaker
was silent.
To see whether the different turn categories
were prosodically distinct from one another, we
applied the following procedure. We first calcu-
lated mean values for each prosodic feature for
each of the four turn categories produced by each
individual speaker. So, for speaker A, we divided
all turns produced into four classes. For each
class, we then calculated mean F0 Max, mean F0
Mean, and so on. After this step had been repeated
for each speaker and for each feature, we then cre-
ated four vectors of speaker means for each indi-
vidual prosodic feature. Then, for each prosodic
feature, we ran a one-factor within subjects anova
on the means to learn whether there was an overall
effect of turn category.
Table 1 shows that, overall, the turn categor-
ies do indeed differ significantly with respect to
different prosodic features; there is a signific-
ant, overall effect of category on F0 Max, RMS
Max, RMS Mean, Duration, Tempo and %Sil. To
identify which pairs of turns were significantly
different where there was an overall significant ef-
fect, we performed posthoc paired t-tests using the
Bonferroni method to adjust the p-level to 0.008
(on the basis of the number of possible pairs that
Turn categories
Feature Normal Correction Aware Corraware a4 -stat
***F0 Max (Hz) 220.05 263.40 216.87 229.00 a4a6a5a8a7a10a9 a11a13a12a15a14 =10.477
F0 Mean (Hz) 161.78 173.43 162.61 158.24 a4a6a5a8a7a10a9 a11a13a12a15a14 =1.575
***RMS Max (dB) 1484.14 1833.62 1538.91 1925.38 a4a6a5a8a7a10a9 a11a13a12a15a14 =7.548
*RMS Mean (dB) 372.47 379.65 425.96 464.16 a4 a5a8a7a10a9 a11a13a12a15a14 =3.190
***Dur (sec) 1.43 4.39 1.12 2.33 a4a6a5a8a7a10a9 a11a13a11a15a14 =34.418
Ppau (sec) 0.60 0.93 0.87 0.80 a4a6a5a8a7a10a9 a11a13a11a15a14 =1.325
**Tempo (syls/sec) 2.59 2.38 2.16 2.43 a4 a5a8a7a10a9 a11a13a11a15a14 =4.206
*%Sil (sec) 0.46 0.41 0.44 0.42 a4 a5a8a7a10a9 a11a13a12a15a14 =3.182
Significance level: *(pa16 .05), **(pa16 .01), ***(pa16 .001)
Table 1: Mean Values of Prosodic Features for Turn Categories.
Prosodic features
Classes F0 max F0 mean RMS max RMS mean Dur Ppau Tempo %Sil
norm/corr - - - +
norm/aware +
norm/corraware - -
aware/corr - - - -
aware/corraware - - -
corraware/corr - -
Table 2: Pairwise Comparisons of Different Turn Categories by Prosodic Feature.
can be drawn from an array of 4 means). Res-
ults are summarized in Table 2, where ‘ + ’ or
‘ - ’ indicates that the feature value of the first cat-
egory is either significantly higher or lower than
the second. Note that, for each of the pairs, there
is at least one prosodic feature that distinguishes
the categories significantly, though it is clear that
some pairs, like aware vs. corr and norm vs. corr
appear to have more distinguishing features than
others, like norm vs. aware. It is also interesting to
see that the three types of post-error turns are in-
deed prosodically different: awares are less prom-
inent in terms of F0 and RMS maximum than cor-
rawares, which, in turn, are less prominent than
corrections, for example. In fact, awares, except
for duration, are prosodically similar to normal
turns.
4 Predictive Results
We next wanted to determine whether the pros-
odic features described above could, alone or
in combination with other automatically avail-
able features, be used to predict our turn categor-
ies automatically. This section describes experi-
ments using the machine learning program RIP-
PER (Cohen, 1996) to automatically induce pre-
diction models from our data. Like many learn-
ing programs, RIPPER takes as input the classes
to be learned, a set of feature names and possible
values, and training data specifying the class and
feature values for each training example. RIPPER
outputs a classification model for predicting the
class of future examples, expressed as an ordered
set of if-then rules. The main advantages of RIP-
PER for our experiments are that RIPPER supports
“set-valued” features (which allows us to repres-
ent the speech recognizer’s best hypothesis as a set
of words), and that rule output is an intuitive way
to gain insight into our data.
In the current experiments, we used 10-fold
cross-validation to estimate the accuracy of the
rulesets learned. Our predicted classes corres-
pond to the turn categories described in Section
2 and variations described below. We repres-
ent each user turn using the feature set shown in
Figure 2, which we previously found useful for
predicting corrections (Hirschberg et al., 2001).
A subset of the features includes the automatic-
ally computable raw prosodic features shown in
Table 1 (Raw), and normalized versions of these
features, where normalization was done by first
turn (Norm1) or by previous turn (Norm2) in a
dialogue. The set labeled ‘ASR’ contains stand-
ard input and output of the speech recognition pro-
cess, which grammar was used for the dialogue
state the system believed the user to be in (gram),
Raw: f0 max, f0 mean, rms max, rms mean, dur, ppau,
tempo, %sil;
Norm1: f0 max1, f0 mean1, rms max1, rms mean1, dur1,
ppau1, tempo1, %sil1;
Norm2: f0 max2, f0 mean2, rms max2, rms mean2, dur2,
ppau2, tempo2, %sil2;
ASR: gram, str, conf, ynstr, nofeat, canc, help, wordsstr,
syls, rejbool;
System Experimental: inittype, conftype, adapt, realstrat;
Dialogue Position: diadist;
PreTurn: features for preceding turn (e.g., pref0max);
PrepreTurn: features for preceding preceding turn (e.g.,
ppref0max);
Prior: for each boolean-valued feature (ynstr, nofeat,
canc, help, rejbool), the number/percentage of
prior turns exhibiting the feature (e.g., prioryn-
strnum/priorynstrpct);
PMean: for each continuous-valued feature, the mean of the
feature’s value over all prior turns (e.g., pmnf0max);
Figure 2: Feature Set.
the system’s best hypothesis for the user input
(str), and the acoustic confidence score produced
by the recognizer for the turn (conf). As subcases
of the str feature, we also included whether or not
the recognized string included the strings yes or no
(ynstr), some variant of no such as nope (nofeat),
cancel (canc), or help (help), as these lexical items
were often used to signal problems in our sys-
tem. We also derived features to approximate the
length of the user turn in words (wordsstr) and in
syllables (syls) from the str features. And we ad-
ded a boolean feature identifying whether or not
the turn had been rejected by the system (rejbool).
Next, we include a set of features representing
the system’s dialogue strategy when each turn was
produced. These include the system’s current ini-
tiative and confirmation strategies (inittype, conf-
type), whether users could adapt the system’s dia-
logue strategies (adapt), and the combined initiat-
ive/confirmation strategy in effect at the time of
the turn (realstrat). Finally, given that our previ-
ous studies showed that preceding dialogue con-
text may affect correction behavior (Swerts et al.,
2000; Hirschberg et al., 2001), we included a fea-
ture (diadist) reflecting the distance of the current
turn from the beginning of the dialogue, and a set
of features summarizing aspects of the prior dia-
logue: for the latter features, we included both the
number of times prior turns exhibited certain char-
acteristics (e.g. priorcancnum) and the percent-
age of the prior dialogue containing one of these
features (e.g. priorcancpct). We also examined
means for all raw and normalized prosodic fea-
tures and some word-based features over the en-
tire dialogue preceding the turn to be predicted
(pmn ). Finally, we examined more local con-
texts, including all features of the preceding turn
(pre ) and for the turn preceding that (ppre ).
We provided all of the above features to the
learning algorithm first to predict the four-way
classification of turns into normal, aware, corr and
corraware. A baseline for this classification (al-
ways predicting norm, the majority class) has a
success rate of 57%. Compared to this, our fea-
tures improve classification accuracy to 74.23%
(+/- 0.96%). Figure 3 presents the rules learned
for this classification. Of the features that appear
in the ruleset, about half are features of current
turn and half features of the prior context. Only
once does a system feature appear, suggesting that
the rules generalize beyond the experimental con-
ditions of the data collection. Of the features spe-
cific to the current turn, prosodic features domin-
ate, and, overall, timing features (dur and tempo
especially) appear most frequently in the rules.
About half of the contextual features are prosodic
ones and half are ASR features, with ASR confid-
ence score appearing to be most useful. ASR fea-
tures of the current turn which appear most often
are string-based features and the grammar state
the system used for recognizing the turn. There
appear to be no differences in which type of fea-
tures are chosen to predict the different classes.
If we express the prediction results in terms of
precision and recall, we see how our classification
accuracy varies for the different turn categories
(Table 3). From Table 3, we see that the majority
class (normal) is most accurately classified. Pre-
dictions for the other three categories, which oc-
cur about equally often in our corpus, vary consid-
erably, with modest results for corr and corraware,
and rather poor results for aware. Table 4 shows a
confusion matrix for the four classes, produced by
if (gram=universal) a17 (dur2 a18 7.31) then CORR
if (dur2 a18 2.19) a17 (priornofeatpct a18 0.09) a17 (tempo a18 1.50) a17 (pmntempo a19 2.39) then CORR
if (dur2 a18 1.53) a17 (pmnwordsstr a18 2.06) a17 (tempo1 a18 1.07) a17 (predur a18 0.80) a17 (prenofeat=F) a17 (presyls a19 4) then CORR
if (predur1 a19 0.26) a17 (dur a18 0.79) a17 (rmsmean2 a18 1.51) a17 (f0mean a19 173.49) then CORR
if (dur2 a18 1.41) a17 (prenofeat=T) a17 (str contains word ‘eight’) then CORR
if (predur1 a19 0.18) a17 (dur2 a18 4.21) a17 (dur1 a19 0.50) a17 (f0mean a19 276.43) then CORR
if (predur1 a19 0.19) a17 (ppregram=cityname) a17 (rmsmax1 a18 1.10) a17 (pmntempo2 a19 1.64) then CORR
if (realstrat=SystemImplicit) a17 (gram=cityname) a17 (pmnf0mean1 a19 0.96) then CORR
if (preconf a19 -2.66) a17 (dur2 a19 0.31) a17 (pprenofeat=T) a17 (tempo2 a18 0.61) then AWARE
if (preconf a19 -2.85) a17 (syls a19 2) a17 (predur a18 1.05) a17 (pref0max a20 4.82) a17 (tempo2 a18 0.58) a17 (pmn%sil a19 0.53) then AWARE
if (preconf a19 -3.34) a17 (syls a19 2) a17 (ppau a18 0.57) a17 (conf a18 -3.07) a17 (preppau a18 0.72) then AWARE
if (dur a18 0.74) a17 (pmndur a18 2.57) a17 (preconf a19 -4.36) a17 (f0mean2 a18 0.90) then CORRAWARE
if (preconf a19 -2.80) a17 (pretempo a19 2.16) a17 (preconf a19 -3.95) a17 (tempo1 a19 4.67) then CORRAWARE
if (preconf a19 -2.80) a17 (dur a18 0.66) a17 (rmsmean a18 488.56) then CORRAWARE
if (preconf a19 -3.56) a17 (dur2 a18 0.64) a17 (prerejbool=T) then CORRAWARE
if (pretempo a19 0.71) a17 (tempo a19 3.31) then CORRAWARE
if (preconf a19 -3.01) a17 (tempo2 a18 0.78) a17 (pmndur a18 2.83) a17 (pmnf0mean a18 199.84) then CORRAWARE
if (pmnconf a19 -3.10) a17 (prestr contains the word ‘help’) a17 (pmndur2 a19 2.01) a17 (ppau a19 0.98) then CORRAWARE
if (pmnconf a19 -3.10) a17 (gram=universal) a17 (pregram=universal) a17 ( %sil a19 0.39) then CORRAWARE
else NORM
Figure 3: Rules for Predicting 4 Turn Categories.
Precision (%) Recall (%)
norm 80.09 89.39
corr 72.86 61.66
aware 61.01 39.79
corraware 61.76 61.72
Accuracy: 74.23% (a21 0.96%); baseline: 57%
Table 3: 4-way Classification Performance.
applying our best ruleset to the whole corpus. This
Classified as
norm corr aware corraware
norm 1263 14 11 38
corr 68 219 0 7
aware 149 1 130 47
corraware 53 5 8 315
Table 4: Confusion Matrix, 4-way Classification.
matrix clearly shows a tendency for the minority
classes, aware, corr and corraware, to be falsely
classified as normal. It also shows that aware and
corraware are more often confused than the other
categories.
These confusability results motivated us to col-
lapse the aware and corraware into one class,
which we will label isaware; this class thus rep-
resents all turns in which users become aware of
a problem. From a system perspective, such a
3-way classification would be useful in identify-
ing the existence of a prior system failure and in
further identifying those turns which simply rep-
resent corrections; such information might be as
useful, potentially, as the 4-way distinction, if we
could achieve it with greater accuracy.
Indeed, when we predict the three classes
(isaware, corr, and norm) instead of four, we
do improve in predictive power — from 74.23%
to 81.14% (+/- 0.83%) classification success.
Again, this compares to the baseline (predicting
norm, which is still the majority class) of 57%. We
also get a corresponding improvement in terms of
precision and recall, as shown in Table 5, with
the isaware category considerably better distin-
guished than either aware or corraware in Table 3.
The ruleset for the 3-class predictions is given in
Precision (%) Recall(%)
norm 84.49 87.48
corr 72.07 67.38
isaware 80.52 77.07
Accuracy: 81.14% (a21 0.83%); baseline: 57%
Table 5: 3-way Classification Performance.
Figure 4. The distribution of features in this rule-
set is quite similar to that in Figure 3. However,
there appear to be clear differences in which fea-
tures best predict which classes. First, the features
used to predict corrections are balanced between
those from the current turn and features from the
preceding context, whereas isaware rules primar-
ily make use of features of the preceding context.
Second, the features appearing most often in the
rules predicting corrections are durational features
(dur2, predur1, dur), while duration is used only
if (gram=universal) a17 (dur2 a18 7.31) then CORR
if (dur2 a18 2.25) a17 (priornofeatpct a18 0.11) a17 (%sil a19 0.55)
a17 (wordsstr a18 4) then CORR
if (dur2 a18 2.75) a17 (gram=universal) a17 (pre%sil1 a18 1.17)
then CORR
if (predur1 a19 0.24) a17 (dur a18 0.85) a17 (priornofeatnum a18 2)
a17 (pmnconf a18 -3.11) a17 (pmn%sil a19 0.45) then CORR
if (predur1 a19 0.19) a17 (dur a18 1.21) a17 (pmnf0mean2 a18 0.99)
a17 (predur2 a19 0.90) a17 (%sil a19 0.70) a17 (tempo a19 3.25) then
CORR
if (predur1 a19 0.20) a17 (ynstr=F) a17 (pregram=cityname) a17
(ppref0mean a18 171.58) then CORR
if (dur2 a18 0.75) a17 (gram=cityname) a17 (pmnsyls a18 3.67) a17
(pmnconf a18 -3.23) a17 (%sil a18 0.41) then CORR
if (prenofeat=T) a17 (preconf a18 -0.72) then CORR
if (preconf a19 -4.07) then ISAWARE
if (preconf a19 -2.76) a17 (pmntempo a19 2.39) a17 (tempo2 a18
1.56) a17 (preynstr=F) then ISAWARE
if (preconf a19 -2.75) a17 (ppau a18 0.46) a17 (tempo a19 1.20) then
ISAWARE
if (pretempo a19 0.23) then ISAWARE
if (pmnconf a19 -3.10) a17 (ppregram=universal) a17 (ppre%sil a19
0.34) a17 (tempo1 a19 2.94) then ISAWARE
if (predur a18 1.27) a17 (pretempo a19 2.36) a17 (prermsmean a18
229.33) a17 (tempo2 a18 0.83) then ISAWARE
if (preconf a19 -2.80) a17 (nofeat=T) a17 (f0mean a19 205.56) then
ISAWARE
else NORM
Figure 4: Rules for Predicting 3 Turn Categories.
once in isaware rules. Instead, these rules make
considerable use of the ASR confidence score of
the preceding turn; in cases where aware turns im-
mediately follow a rejection or recognition error,
one would expect this to be true. Isaware rules
also appear distinct from correction rules in that
they make frequent use of the tempo feature. It
is also interesting to note that rules for predicting
isaware turns make only limited use of the nofeat
feature, i.e. whether or not a variant of the word
no appears in the turn. We might expect this lex-
ical item to be a more useful predictor, since in
the explicit confirmation condition, users should
become aware of errors while responding to a re-
quest for confirmation.
Note that corrections, now the minority class,
are more poorly distinguished than other classes in
our 3-way classification task (Table 5). In a third
set of experiments, we merged corrections with
normal turns to form a 2-way distinction over all
between aware turns and all others. Thus, we only
distinguish turns in which a user first becomes
aware of an ASR failure (our original isaware and
corraware categories) from those that are not (our
original corr and norm categories). Such a dis-
tinction could be useful in flagging a prior sys-
tem problem, even though it fails to target the ma-
terial intended to correct that problem. For this
new 2-way distinction, we obtain a higher de-
gree of classification accuracy than for the 3-way
classification — 87.80% (+/- 0.61%) compared to
81.14%. Note, however, that the baseline (predict
majority class of !isaware) for this new classifica-
tion is 70%, considerably higher than the previous
baseline. Table 6 shows the improvement in terms
of accuracy, precision, and recall.
Precision (%) Recall (%)
!isaware 91.7 91.6
isaware 80.7 81.1
Accuracy: 87.80% (a21 0.61%); baseline: 70%
Table 6: 2-way Classification Performance.
The ruleset for the 2-way distinction is shown in
Figure 5. The features appearing most frequently
if (preconf a19 -4.06) a17 (pretempo a19 2.65) a17 (ppau a18 0.25)
then T
if (preconf a19 -3.59) a17 (prerejbool=T) then T
if (preconf a19 -2.85) a17 (predur a18 1.039) a17 (tempo2 a18 1.04)
a17 (preppau a18 0.57) a17 (pretempo a19 2.18) then T
if (preconf a19 -3.78) a17 (pmnsyls a18 4.04) then T
if (preconf a19 -2.75) a17 (prestr contains the word ‘help’) then
T
if (pregram=universal) a17 (pprewordsstr a18 2) then T
if (preconf a19 -2.60) a17 (predur a18 1.04) a17 (%sil1 a19 1.06) a17
(prermsmean a18 370.65) then T
if (pretempo a19 0.13) then T
if (predur a18 1.27) a17 (pretempo a19 2.36) a17 (prermsmean a18
245.36) then T
if (pretempo a19 0.80) a17 (pmntempo a19 1.75) a17 (ppretempo2
a19 1.39) then T
then F
Figure 5: Rules for Predicting 2 Turn Categories:
ISAWARE (T) versus the rest (F).
in these rules are similar to those in the previous
two rulesets in some ways, but quite different in
others. Like the rules in Figures 3 and 4, they ap-
pear independent of system characteristics. Also,
of the contextual features appearing in the rules,
about half are prosodic features and half ASR-
related; and, of the current turn features, pros-
odic features dominate. And timing features again
(especially tempo) dominate the prosodic features
that appear in the rules. However, in contrast to
previous classification rulesets, very few features
of the current turn appear in the rules at all. So,
it would seem that, for the broader classification
task, contextual features are far more important
than for the more fine-grained distinctions.
5 Conclusion
Continuing our earlier research into the use of
prosodic information to identify system misrecog-
nitions and user corrections in a SDS, we have
studied aware sites, turns in which a user first no-
tices a system error. We find first that these sites
have prosodic properties which distinguish them
from other turns, such as corrections and normal
turns. Subsequent machine learning experiments
distinguishing aware sites from corrections and
from normal turns show that aware sites can be
classified as such automatically, with a consid-
erable degree of accuracy. In particular, in a 2-
way classification of aware sites vs. all other turns
we achieve an estimated success rate of 87.8%.
Such classification, we believe, will be especially
useful in error-handling for SDS. We have pre-
viously shown that misrecognitions can be clas-
sified with considerable accuracy, using prosodic
and other automatically available features. With
our new success in identifying aware sites, we
acquire another potentially powerful indicator of
prior error. Using these two indicators together,
we hope to target system errors considerably more
accurately than current SDS can do and to hypo-
thesize likely locations of user attempts to correct
these errors. Our future research will focus upon
combining these sources of information identify-
ing system errors and user corrections, and invest-
igating strategies to make use of this information,
including changes in dialogue strategy (e.g. from
user or mixed initiative to system initiative after
errors) and the use of specially trained acoustic
models to better recognize corrections.

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