Spoken Dialogue Systems

1. Spoken Dialogue Systems CS 4705

2. Talking to a Machine….and (often) Getting an Answer • Today’s spoken dialogue systems make it possible to accomplish real taskswithout talking to a person • Could Eliza do this? • What do today’s systems do better? • Do they actually embody human intelligence? • Key advances • Stick to goal-directed interactions in a limited domain • Prime users to adopt the vocabulary you can recognize • Partition the interaction into manageable stages • Judicious use of system vs. mixed initiative

3. Dialogue vs. Monologue • Monologue and dialogue both involve interpreting • Information status • Coherence issues • Reference resolution • Speech acts, implicature, intentionality • Dialogue involves managing • Turn-taking • Grounding and repairing misunderstandings • Initiative and confirmation strategies

4. Segmenting Speech into Utterances • What is an `utterance’? • Why is EOU detection harder than EOS? • How does speech differ from text? • Single syntactic sentence may span several turns A: We've got you on USAir flight 99 B: Yep A: leaving on December 1. • Multiple syntactic sentences may occur in single turn A: We've got you on USAir flight 99 leaving on December. Do you need a rental car? • Intonational definitions: intonational phrase, breath group, intonation unit

5. Turns and Utterances • Dialogue is characterized by turn-taking: who should talk next, and when they should talk • How do we identify turns in recorded speech? • Little speaker overlap (around 5% in English --although depends on domain) • But little silence between turns either • How do we know when a speaker is giving up or taking a turn? Holding the floor? How do we know when a speaker is interruptable?

6. Simplified Turn-Taking Rule (Sacks et al) • At each transition-relevance place (TRP) of each turn: • If current speaker has selected A as next speaker, then A must speak next • If current speaker does not select next speaker, any other speaker may take next turn • If no one else takes next turn, the current speaker may take next turn • TRPs are where the structure of the language allows speaker shifts to occur

7. Adjacencypairs set up next speaker expectations • GREETING/GREETING • QUESTION/ANSWER • COMPLIMENT/DOWNPLAYER • REQUEST/GRANT • ‘Significant silence’ is dispreferred A: Is there something bothering you or not? (1.0s) A: Yes or no? (1.5s) A: Eh? B: No.

8. Intonational Cues to Turntaking • Continuation rise (L-H%) holds the floor • H-H% requests a response • L*H-H% (ynq contour) • H* H-H% (highrise question contour) • Intonational contours signal dialogue acts in adjacency pairs

9. Timing and Turntaking • How should we time responses in a SDS? • Japanese studies of aizuchi (backchannels) (Koiso et al ‘98, Takeuchi et al ‘02) in natural speech • Lexical information: particles ne and ka ending preceding turn or (in telephone shopping) product names • Length of preceding utterance, f0, loudness, and pause after even more important in predicting turntaking

10. Turntaking and Initiative Strategies • System Initiative S: Please give me your arrival city name. U: Baltimore. S: Please give me your departure city name…. • User Initiative S: How may I help you? U: I want to go from Boston to Baltimore on November 8. • `Mixed’ initiative S: How may I help you? U: I want to go to Boston. S: What day do you want to go to Boston?

11. Grounding (Clark & Shaefer ‘89) • Conversational participants don’t just take turns speaking….they try to establish common ground (or mutual belief) • Hmust ground a S's utterances by making it clear whether or not understanding has occurred • How do hearers do this? S:I can upgrade you to an SUV at that rate. • Continued attention (U gazes appreciatively at S) • Relevant next contribution U: Do you have a RAV4 available?

12. Acknowledgement/backchannel U: Ok/Mhmmm/Great! • Demonstration/paraphrase U: An SUV. • Display/repetition U: You can upgrade me to an SUV at the same rate? • Request for repair U: I beg your pardon?

13. Detecting Grounding Behavior • Evidence of system misconceptions reflected in user responses (Krahmer et al ‘99, ‘00) • Responses to incorrect verifications • contain more words (or are empty) • show marked word order (especially after implicit verifications) • contain more disconfirmations, more repeated/corrected info • ‘No’ after incorrect verifications vs. other ynq’s • has higher boundary tone • wider pitch range • longer duration • longer pauses before and after • more additional words after it

14. User information state reflected in response (Shimojima et al ’99, ‘01) • Echoic responses repeat prior information – as acknowledgment or request for confirmation S1: Then go to Keage station. S2: Keage. • Experiment: • Identify ‘degree of integration’ and prosodic features (boundary tone, pitch range, tempo, initial pause) • Perception studies to elicit ‘integration’ effect • Results: fast tempo, little pause and low pitch signal high integration

15. Grounding and Confirmation Strategies U: I want to go to Baltimore. • Explicit S: Did you say you want to go to Baltimore? • Implicit S: Baltimore. (H* L- L%) S: Baltimore? (L* H- H%) S: What time do you want to leave Baltimore? • No confirmation

16. How do we evaluate Dialogue Systems? • PARADISE framework (Walker et al ’00) • “Performance” of a dialogue system is affected both by whatgets accomplished by the user and the dialogue agent and howit gets accomplished Maximize Task Success Minimize Costs Efficiency Measures Qualitative Measures

17. What metrics should we use? • Efficiency of the Interaction:User Turns, System Turns, Elapsed Time • Quality of the Interaction: ASR rejections, Time Out Prompts, Help Requests, Barge-Ins, Mean Recognition Score (concept accuracy), Cancellation Requests • User Satisfaction • Task Success: perceived completion, information extracted

18. Was Annie easy to understand in this conversation? (TTS Performance) In this conversation, did Annie understand what you said? (ASR Performance) In this conversation, was it easy to find the message you wanted? (Task Ease) Was the pace of interaction with Annie appropriate in this conversation? (Interaction Pace) In this conversation, did you know what you could say at each point of the dialog? (User Expertise) How often was Annie sluggish and slow to reply to you in this conversation? (System Response) Did Annie work the way you expected her to in this conversation? (Expected Behavior) From your current experience with using Annie to get your email, do you think you'd use Annie regularly to access your mail when you are away from your desk? (Future Use) User Satisfaction:Sum of Many Measures

19. Performance Model • Weights trained for each independent factor via multiple regression modeling: how much does each contribute to User Satisfaction? • Result useful for system development • Making predictions about system modifications • Distinguishing ‘good’ dialogues from ‘bad’ dialogues • But … can we also tell on-line when a dialogue is ‘going wrong’

20. Identifying Misrecognitions, Awares and User Corrections Automatically (Hirschberg, Litman & Swerts) • Collect corpus from interactive voice response system • Identify speaker ‘turns’ • incorrectly recognized • where speakers first aware of error • that correct misrecognitions • Identify prosodic features of turns in each category and compare to other turns • Use Machine Learning techniques to train a classifier to make these distinctions automatically

21. Turn Types TOOT: Hi. This is AT&T Amtrak Schedule System. This is TOOT. How may I help you? User: Hello. I would like trains from Philadelphia to New York leaving on Sunday at ten thirty in the evening. TOOT: Which city do you want to go to? User: New York. misrecognition correction aware site

22. Results • Reduced error in predicting misrecognized turns to 8.64% • Error in predicting ‘awares’ (12%) • Error in predicting corrections (18-21%)

23. Conclusions • Spoken dialogue systems presents new problems -- but also new possibilities • Recognizing speech introduces a new source of errors • Additional information provided in the speech stream offers new information about users’ intended meanings, emotional state (grounding of information, speech acts, reaction to system errors) • Why spoken dialogue systems rather than web-based interfaces?