SDS Future

1. SDS Future Julia Hirschberg LSA07 353

2. Today • Whither Spoken Dialogue Systems? • Technology issues • Human factors issues • Taking automated dialogue to the next level • Modeling users’ emotional state • Entrainment/adaptation/… to users • System personality • Cultural sensitivity

3. Technology Issues • Better ASR • Fast and accurate • Better rejection capabilities • Trained on real dialogue phenomena (hyper-articulation, self-repairs and other disfluencies) and broader subject pool • More sophisticated semantic representations • Automated call routing • Tools to automate creation of new systems • Recognizers • More accurate • Easier to train • Dialogue flow schemes • TTS voices

4. Human Factors Issues • Improved modeling of human/human interaction • Better model of turn-taking (e.g. backchanneling behavior, timing issues) • Incorporation of dialogue act disambiguation findings • Error detection/correction capabilities • Support for mixed initiative • Moving away from the voice menu model • Better ways to evaluate system performance • Adaptation/customization for frequent/power users

5. Today • Whither Spoken Dialogue Systems? • Technology issues • Human factors issues • Taking automated dialogue to the next level • Modeling users’ emotional state • Entrainment/adaptation/… to users • System personality • Cultural sensitivity

6. Emotion and Speaker State • A speaker’s emotional state represents important and useful information • To recognize • Anger/frustration in call center SDS • Confidence/uncertainty in a tutoring domain • To generate (e.g. any emotion for games) • Prosodic information has proven quite useful in detecting different emotions automatically

7. Studies of Emotional Speech in Human/Human Corpora • Anger/frustration • Travel scenarios (Batliner et al (2003), Ang et al (2002)) • Call Centers (Liscombe et al (2005), Vidrascu & Devillers (2005), Lee & Narayanan (2005)) • Other emotions • Meetings (Wrede & Shriberg (2003)) • Unconstrained (Roach (2000), Cowie et al (2001), Campbell (2003),…)

8. Issues in Emotional Speech Studies • Data debate: • Acted speech vs. natural (hand labeled) corpora • Classification tasks: • Distinguish specific ‘classic’ emotions • Distinguish negative emotions • Distinguish valence, activation • Representations of prosodic features • Direct modeling via acoustic correlates • A symbolic representation (e.g. ToBI)

9. Acted Speech: LDC Emotional Speech Corpus happy sad angry confident frustrated friendly interested anxious bored encouraging

10. Can We Distinguish Classic Emotions in Acted Speech? • User study to classify tokens from LDC corpus • 10 emotions: • Positive: confident, encouraging, friendly, happy, interested • Negative: angry, anxious, bored, frustrated, sad • Chosen from most convincing • Machine learning classification of tokens by majority label (binary classification) • (Liscombe, Hirschberg & Venditti, 2003)

11. What Features Are Useful in Emotion Classification? • Features: • Automatically extracted pitch, intensity, rate • Spectral tilt from hand-segmented vowels • Hand-labeled ToBI contours • Results: • Direct modeling of acoustic/prosodic features • 62% average baseline • 75% average accuracy • Acoustic-prosodic features identify activation • Higher-level ToBI features distinguish valence • H-L% correlated with negative emotions • L-L% with positive

12. Accuracy Distinguishing One Emotion from the Rest: Direct Modeling

13. Different Valence/Different Activation

14. Different Valence/ Same Activation

15. Can We Identify Emotions in Natural Speech? • AT&T’s “How May I Help You?” system • Liscombe, Guicciardi, Tur & Gokken-Tur ‘05 • Customers are sometimes angry, frustrated • Data: • 5690 operator/caller dialogues with 20,013 caller turns • Labeled for degrees of anger, frustration, negativity and collapsed to positive vs. frustrated vs. angry

16. HMIHY Example VeryFrustrated Somewhat Frustrated

17. Features • Automatic acoustic-prosodic • Lexical • Pragmatic (labeled DAs) • Contextual (all above features for preceding 1 or 2 turns)

18. Direct Modeling of Prosody Features in Context

19. Direct Modeling of Prosodic Features in Context

20. Results

21. Implications for SDS • SDS should be able to take advantage of current imperfect emotion prediction capabilities • Even if you miss some angry people…. • E.g. Some call center software monitors conversations for regions of high intensity

22. Today • Whither Spoken Dialogue Systems? • Technology issues • Human factors issues • Taking automated dialogue to the next level • Modeling users’ emotional state • Entrainment/adaptation/… to users • System personality • Cultural sensitivity

23. Entrainment/Adaptation/Accommodation/Alignment • Hypothesis: over time, people tend to adapt their communicative behavior to that of their conversational partner • Issues • What are the dimensions of entrainment? • How rapidly do people adapt? • Does entrainment occur (on the human side) in human/computer conversations?

24. Varieties of Entrainment… • Lexical: S and H tend over time to adopt the same method of referring to items in a discourse A: It’s that thing that looks like a harpsichord. B: So the harpsichord-looking thing… .... B: The harpsichord… • Phonological • Word pronunciation: voice/voiceless /t/ in better • Acoustic/Prosodic • Speaking rate, pitch range, choice of contour • Discourse/dialogue/social • Marking of topic shift, turn-taking

25. The Vocabulary Problem • Furnas et al ’87: the probability that 2 subjects will producing the same name for a command for common computer operations varied from .07-.18 • Remove a file: remove, delete, erase, kill, omit, destroy, lose, change, trash • With 20 synonyms for a single command, the likelihood that 2 people will choose the same one was 80% • With 25 commands, the likelihood that 2 people who choose the same term think it means the same command was 15% • How can people possibly communicate? • They collaborate on choice of referring expressions

26. Early Studies of Priming Effects • Hypothesis: Users will tend to use the vocabulary and syntax the system uses • Evidence from data collections in the field • Systems should take advantage of this proclivity to prime users to speak in ways that the system can recognize well

27. User Responses to Vaxholm The answers to the question: “What weekday do you want to go?” (Vilken veckodag vill du åka?) • 22% Friday (fredag) • 11% I want to go on Friday (jag vill åka på fredag) • 11% I want to go today (jag vill åka idag) • 7% on Friday (på fredag) • 6% I want to go a Friday (jag vill åka en fredag) • - are there any hotels in Vaxholm?(finns det några hotell i Vaxholm)

28. Verb Priming: How often do you go abroad on holiday? Hur ofta åker du utomlands på semestern? Hur ofta reser du utomlands på semestern? jag reser en gång om året utomlands jag reser inte ofta utomlands på semester det blir mera i arbetet jag reserreser utomlands på semestern vartannat år jag reser utomlands en gång per semester jag reser utomlands på semester ungefär en gång per år jag brukar resa utomlands på semestern åtminståne en gång i året en gång per år kanske en gång vart annat år varje år vart tredje år ungefär nu för tiden inte så ofta varje år brukar jag åka utomlands jag åker en gång om året kanske jag åker ganska sällan utomlands på semester jag åker nästan alltid utomlands under min semester jag åker ungefär 2 gånger per år utomlands på semester jag åker utomlands nästan varje år jag åker utomlands på semestern varje år jag åker utomlands ungefär en gång om året jag är nästan aldrig utomlands en eller två gånger om året en gång per semester kanske en gång per år ungefär en gång per år åtminståne en gång om året nästan aldrig

29. Results no reuse no answer 4% 2% other 24% reuse 52% 18% ellipse

30. Lexical Entrainment in Referring Expressions • Choice of Referring Expressions: Informativeness vs. availability (basic level or not) vs. saliency vs. recency • Gricean prediction • People use descriptions that minimally but effectively distinguish among items in the discourse • Garrod & Anderson ’87 Output/Input Principle • Conversational partners formulate their current utterance according to the model used to interpret their partner’s most recent utterance • Clark, Brennan, et al’s Conceptual Pacts • People make Conceptual Pacts wrt appropriate referring expressions made with particular conversational partners • They are loath to abandon these even when shorter expressions possible

31. Entrainment in Spontaneous Speech S13: the orange M&M looking kind of scared and then a one on the bottom left and a nine on the bottom right S12: alright I have the exact same thing I just had it's an M&M looking scared that's orange S13: yeah the scared M&M guy yeah S12: framed mirror and the scared M&M on the lower right S13: and it's to the right of the scared M&M guy S13: yeah and the iron should be on the same line as the frightened M&M kind of like an L S12: to the left of the scared M&M to the right of the onion and above the iron

32. Extraterrestrial vs Alien I s11: okay in the middle of the card I have an extraterrestrial figure s11: okay middle of the card I have theextraterrestrial … s10: I've got the blue lion with the extraterrestrial on the lower right s11: okay I have the extraterrestrial now and then I have the eye at the bottom right corner s10: my extraterrestrial's gone

33. Extraterrestrial vs. Alien II S03: okay I have a blue lion and then the extraterrestrial at the lower right corner S11: mm I'll pass I have the alien with an eye in the lower right corner S03: um I have just the alien so I guess I'll match that ------------------------------------------------------------------------------ S10: yes now I've got that extraterrestrial with the yellow lion and the money … S12: oh now I have the blue lion in the center with our little alienbuddy in the right hand corner S10: with the alien buddy so I'm gonna match him with the single blue lion okay I've got our alien with the eye in the corner

34. Timing and Voice Quality • Guitar & Marchinkoski ’01: • How early do we start to adapt to others’ speech? • Do children adapt their speaking rate to their mother’s speech? • Study: • 6 mothers spoke with their own (normally speaking) 3-yr-olds (3M, 3F) • Mothers’ rates significantly reduced (B) or not (A) in A-B-A-B design • Results: • 5/6 children reduced their rates when their mothers spoke more slowly

35. Sherblom & La Riviere ’87: How are speech timing and voice quality affected by a non-familiar conversational partner? • Study: • 65 pairs of undergraduates asked to discuss a ‘problem situation’ together • Utter a single sentence before and after the conversation • Sentences compared for speaking rate, utterance length and vocal jitter • Results: • Substantial influence of partner on all 3 measures • Interpersonal uncertainty and differences in arousal influenced degree of adaptation

36. Amplitude and Response Latency • Coulston et al ’02: • Do humans adapt to the behavior of non-human partners? • Do children speak more loudly to a loud animated character? • Study: • 24 7-10-yr olds interacted with an extroverted, loud animated character and with an introverted, soft character (TTS voices) • Multiple tasks using different amplitude ranges • Human/TTS amplitudes and latencies compared • Results: • 79-94% of children adapted their amplitude, bi-directionally • Also adapted their response latencies (mean 18.4%), bidirectionally

37. Social Status and Entrainment • Azuma ’97: Do speakers adapt to the style of other social classes? • Study: Emperor Hirohito visits the countryside • Corpus-based study of speech style of Japanese Emperor Hirohito during chihoo jyunkoo (`visits to countryside‘), 1946-54 • Published transcripts of speeches • Findings: • Emperor Hirohito converged his speech style to that of listeners lower in social status • Choice of verb-forms, pronouns no longer those of person with highest authority • Perceived as like those of a (low-status) mother

38. Socio-Cultural Influences and Entrainment • Co-teachers adapt teaching styles (Roth ’05) • Social context • High school in NE with predominantly African-American student body • Cristobal: Cuban-African-American teacher • Chris: new Italian-American teacher • Adaptation of Chris to Cristobal • Catch phrases (e.g. right!, really really hot) and their production: pitch and intensity contours • Pitch ‘matching’ across speakers • Mimesis vs entrainment

39. Conclusions for SDS • Systems can make use of user tendency to entrain to system vocabulary • Should systems also entrain to their users? • CMU’s Let’s Go system adapts confirmation prompts to non-native speech, finding the closest match to user input in its own vocabulary

40. Today • Whither Spoken Dialogue Systems? • Technology issues • Human factors issues • Taking automated dialogue to the next level • Modeling users’ emotional state • Entrainment/adaptation/… to users • System personality • Cultural sensitivity

41. Personality and Computer Systems • Early-pc-era reports that significant others were jealous of the time their partners spent with their computers. • Reeves & Nass, The Media Equation How People Treat Computers, Television, and New Media Like Real People and Places, 1996 • Evolution explains the anthropomorphization of the pc • Humans evolved over millions of years without media • Proper response to any stimulus was critical to survival • Human psychology and physiological responses well developed before media invented • Ergo, our bodies and minds react to media, immediately and fundamentally, as if they were real

42. People See ‘Personality’ Everywhere • Humans assess personality of another (human or otherwise) quickly, with minimal clues • Perceived computer personality strongly affects how we evaluate the computer and information it provides • Experiments: • Created “dominant” and “submissive” computer interfaces and asked subjects to use to solve hypothetical problems • Max (dominant) used assertive language, showed higher confidence in the information displayed (via a numeric scale), always presented its own analysis of the problem first • Linus (submissive) phrased information more tentatively, rated its own information at lower confidence levels, and allowed human to discuss problem first • Each used alternately by people whose personalities previously identified as being either dominant or submissive

43. User Reactions • Users described Max and Linus in human terms: aggressive, assertive, authoritative vs. shy, timid, submissive • Users correctly identified machines more like themselves • Users rated machines more like themselves as better computers even though content received exactly the same. • Users rated their own performance better when machine’s personality matched theirs • People more frank when rating a computer if questionnaire presented on another machine • Subjects thought highly of computers that praised them, even if praise clearly undeserved

44. Personality in SDS • Mairesse & Walker ’07 PERSONAGE (PERSONAlity GEnerator) • ‘Big 5’ personality trait model: extroversion, neuroticism, agreeableness, conscientiousness, openness to experience • Attempts to generate “extroverted” language based on traits associated with extroversion in psychology literature • Demo: find your personality type

46. Conclusions for SDS • Systems can be designed to convey different personalities • Can they recognize users’ personalities and entrain to them? • Should they?

47. Goodbye! • Final Paper