 Spoken dialogue systems (SDS) are prone to error, especially because of errors in the ASR component of such systems
 Errors will remain a problem for future systems, e.g. when they have to operate in noisy conditions, with non-native speakers or when the domain of the system becomes larger
 Therefore: key task for most dialogue managers in SDS systems is error handling:
– Prevent errors (e.g. optimal dialogue strategies)
– Detect errors (e.g. acoustic and semantic confidence scores)
– Correct errors (e.g. feedback cues, system prompts)

 Recent interest in the use of speech prosody for error handling
– To detect misrecognized utterances which have been shown to be prosodically different from correctly recognized utterances (e.g. Hirschberg et al. 2004)
– To distinguish positive from negative feedback cues about the smoothness of the interaction (e.g. Krahmer et al. 2002)
– To locate places where speakers try to correct a prior utterance (corrections tend to be hyperarticulated, which often leads to ‘spiral’ errors) (Oviatt et al.
1998)
 Previous research only focused on verbal features; in this talk we concentrate on the effect of errors on visual features as well (audiovisual prosody)

 Report on analyses of interactions between speakers and their dialogue partners (both humans and machine)
 Study audiovisual features of speakers
– When speakers notice they themselves have a problem (Part 1)
– When speakers notice their dialogue partners have a problem (Part 2)

 What are audiovisual features of a speaker who experiences communication problems?

 Speakers are not always equally confident about or committed to what they are saying
 Suppose someone asks a question (Who wrote hamlet? What is the capital of Switzerland?)
– Speakers may be sure about their answer, or rather uncertain
– Speakers may not know the answer, though it may be on the tip of their tongue
 These differences in confidence level are reflected in the way speakers present themselves; this is useful for their addressees

 How can visual cues from a speaker’s face be used as signals of level of uncertainty? How important are such cues compared to auditory cues?
 Are their significant differences between different kinds of speakers in their use of visual cues for uncertainty? (here: age differences)

 Experiment in three stages (Hart 1965):
1.
Answers to factual questions ( WISC, WAISC, Trivial Pursuit ).
2.
Test how certain subject is (s)he would recognize the correct answer in a multiplechoice test ( Feeling of Knowing (FOK)-scores ).
3.
Recognition test ( Multiple-choice ).
 “Tip of the tongue”: non-answer ( “I don’t know” ) with a high FOK.
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

Subjects were filmed during first test; they could not see the experimentor.
Adults: person with highest score got a small reward.
Children all got a small award

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20 adults
Students and collegues [20 – 50]
40 questions n = 800



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Who wrote Hamlet?
How many degrees in a circle?
What is the capital of Switzerland?
...
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20 children
Group 4 [7 – 8]
30 questions n = 600
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Who is the president of the U.S.?
Where can you buy a Happy Meal?
What is the color of peanut butter?
...

 All 1400 utterances were manually labelled by 4 independent judges.
 Consensus labeling of presence/absence of different audiovisual features.
 Verbal: high intonation, filled pauses, delay, number of words.
 Visual: eyebrow, smile, “funny face”, gaze [adults only]

FOK correlation
Words
Gaze acts
Marked features
Answers
-.344
-.309
-.422
Non-answers
.401
.347
.462
 Answers: Presence of filled pause, delay, high intonation, eyebrow, smile, funny face and different gaze acts correspond with significantly lower FOK score.
 Non-answers: Presence of filled pause, delay, high intonation, eyebrow, smile, funny face and different gaze acts correspond with significantly higher FOK score

 Answers: Presence of eyebrow, funny face and delay correspond with significantly lower FOK score.
 Non-answers: Presence of smile corresponds with a significantly higher
FOK score.
 Other than that no significant findings.
 In general: children are much less expressive than adults, use occasionally very long delays, and hardly any filled pauses.

 Speakers express their level of uncertainty via various audiovisual cues.
 Adults do this much more than children (‘self-presentation’)
 Opposite findings for answers and nonanswers.
 How is uncertainty perceived? What are the important features?
– In different modalities?
– By different judges?

 Stimuli: 60 adult responses from Experiment 1.
High FOK
Low FOK
Answers Non-answers
15 15
15 15
 120 subjects participated:
Vision+sound
40
Sound only
40
Vision only
40
 Task: judge level of uncertainty of speaker (FOAK scores).

1
0,8 high FOK low FOK
0,6
0,4
0,2
0 answer nonanswer

1
0,8
0,6
0,4
0,2
0
Vision+Sound Sound only Vision only high FOK low FOK

 Observers can estimate a speaker’s level of uncertainty on the basis of audiovisual cues.
 Answers are “easier” than nonanswers.
 Scores for unimodal stimuli are good (both sound only and vision only), but those for bimodal stimuli go best.



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For different speakers/judges: adults vs. children
Same task: judge level of (un)certainty
Stimuli: only answers, selected from experiment 1.
High FOK
Low FOK
Child answers
15
15
Adult-answers
15
15
 80 subjects participated
Adult judge
Child judge
Adult speaker
20
20
Child speaker
20
20

1
0,8
0,6
0,4
0,2
0 adults children adults children adult judges child judges high FOK low FOK

 Adults are “better” judges than children.
(Detecting behavior one does not display is more difficult..)
 Adults are “better” judged than children.
(What is not signalled cannot be detected.)

 What are audiovisual features of a speaker who notices that his/her dialogue partner has communication problems?

 Dialogue partners continuously send and receive signals on the status of the information which is being exhanged
– Positive feedback cues (‘go on’) when there are no problems
– Negative feedback cues (‘go back’) when there are problems
 Previous research revealed that negative feedback cues are prosodically ‘marked’ (e.g. higher, louder, longer) (e.g. Krahmer et al.
2002, Shimojima et al. 2002)
 Here: series of experiments to investigate whether speakers use visual cues as well as auditory ones for distinguishing positive from negative cues

 Taken from an audiovisual corpus of 9 subjects engaged in telephone conversations with a speaker-independent traintime table information system; they had to query the system on 7 train journeys (63 interactions)
 Subjects were video-taped during their interactions; they were led to believe the data collection for the development of a new video-phone
 76% of the dialogues were successfully completed; 374 out of 1183 speaker turns were misunderstood by the system (32%)

 We performed three perception experiments in which 66 subjects were shown selected video-clips from these recorded human-machine interactions
 The clips constituted ‘minimal pairs’, in that they consisted of comparable utterances that had originally occurred either in a problematic or in an unproblematic dialogue exchange
 The subjects’ task was to guess whether the presented clip came from a problematic or unproblematic context

 Subjects saw users listening to verification questions from the system
(so users are silent), which can be unproblematic (such as in 1), or problematic (such as in 2)
1. User: Amsterdam
System: So you want to travel to Amsterdam?
2. User: Amsterdam
System: So you want to travel to Rotterdam?

 Subjects saw speakers uttering a destination; this could the speaker’s first attempt (unproblematic) (like in 1), or it could be a correction in response to a verification question of misrecognized or misunderstood information (like in 2)
1. System: To which station do you want to travel?
User: Rotterdam
2. System: So you want to travel to Amsterdam?
User: Rotterdam

 Subjects saw speakers uttering a negation (“nee”, no ), which could be a response to a general yes-no question (like in 1), or a response to a verification question which contains incorrect information (like in 2)
1. System: Do you want me to repeat the connection?
User: No
2. System: So you want to travel to Amsterdam?
User: No

 In all three studies, subjects were able to correctly distinguish problematic from unproblematic fragments above chance level (task was easier for verification stimuli, and slot fillers)
 In order to gain insight into the audiovisual features that may have functioned as cues we labeled the data in terms of level of hyperarticulation (6 levels), and presence or absence of a number of visual features (most important: smile, head movement, diverted gaze, frown, brow raise)
 Both level of hyperarticulation and relative number of visual cues were correlated with perceived and actual problems

Perceived problems Actual problems

Perceived problems Actual problems

 Dialogue problems have been shown to have consequences for audiovisual characteristics of a speaker who experiences problems him/herself or who notices that the dialogue partner has communication problems
 In general, it appears that problematic dialogue situations lead to more dynamic facial expressions and marked prosodic behaviour

 Research reported here joint work with Emiel Krahmer, Pashiera
Barkhuysen (PhD project) and Lennard van de Laar (technical assistant) within the FOAP (“Functions of audiovisual prosody”) project:
 Other interests: audiovisual cues to end-of-utterance, focus, emotion, deceptive speech, and personality; incorporation of findings in ECAs through collaborations

Adults
Children
Correct answers
Incorrect answers
Non-answers
Correct answers
Incorrect answers
Non-answers n
575
129
96
FOK
0.94
0.76
0.42
n
371
125
131
FOK
0.96
0.74
0.50
Contrary to adults, children have few high FOK non-answers.

 Gain more insight into relevance of visual and auditory cues; because of ceiling effects it was difficult to establish the strength relation between these two types of cues
 Answers (1 HighFOK, 1 LowFOK) from 5 speakers were selected; words had to have a similar sound shape (e.g. Goethe-Goofy; ZurichZorro, …)
 Sound and image were separated to create mixed stimuli (e.g. HighFOK vision combined with LowFOK sound)
 Both original and mixed stimuli were presented to 120 subjects who had to rate the FOK level (7-point scale) of each stimulus

 Overall: bias towards uncertainty in FOK ratings
 FOK ratings are significantly influenced by verbal (intonation pattern) and visual cues from the face; some speaker effect
 However, facial information has much stronger cue value

 Production of uncertainty (based on Smith and Clark 1993):
“Feeling of Knowing” (FOK)
– Experiment 1 : Adults + children
 Perception of uncertainty (based on Brennan and Williams 1995):
“Feeling of Another’s Knowing” (FOAK)
– Experiment 2 : Unimodal vs multimodal
– Experiment 3 : Adults x children

 Integrate the findings in Embodied Conversational Agents in order to make these more natural and believable, in particular for error handling strategies (working hypothesis: Users are more likely to tolerate incorrect answers if the system signals its uncertainty)
 Explore whether visual features can be used as an additional resource for error detection (growing interest in incorporating visual information in automatic recognition process)

 Prosody defined as those features that do not determine what a speaker says, but rather how he or she says it
– Verbal: intonation, tempo, loudness, voice quality, pauses, ….
– Visual: facial expressions, hand and arm gestures, body language, …
 Audiovisual prosody = verbal + visual prosody

 Auditory cues (Smith and Clark, 1993; Brennan and Williams, 1995):
– Linguistic hedges (“I am not sure, but…”, “I think..”)
– Filled pauses (uh and uhm)
– Prosody (question intonation)
 This study: possible visual cues (are natural and important ingredient of daily conversations as well)