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What can humans do when faced with ASR errors?

What can humans do when faced with ASR errors?. Dan Bohus Dialogs on Dialogs Group, October 2003. Question. We’re trying to build systems that can deal with a noisy recognition channel Q: How good are humans are that? More importantly, how do they do it? What strategies do they use?

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What can humans do when faced with ASR errors?

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  1. What can humans do when faced with ASR errors? Dan Bohus Dialogs on Dialogs Group, October 2003

  2. Question • We’re trying to build systems that can deal with a noisy recognition channel • Q: How good are humans are that? • More importantly, how do they do it? • What strategies do they use? • How do they decide which one to use when? • What kind of knowledge used in the process?

  3. WOZ experiments • Modify the WOZ setting so that the wizard does not hear the user, but rather receives the recognition result (text in these cases) • Exploring Human Error Handling Strategies [Gabriel Skantze] • A Study of Human Dialogue Strategies in the Presence of Speech Recognition Errors [Teresa Zollo]

  4. WOZ experiments • Modify the WOZ setting so that the wizard does not hear the user, but rather receives the recognition result (text in these cases) • Exploring Human Error Handling Strategies [Gabriel Skantze] • A Study of Human Dialogue Strategies in the Presence of Speech Recognition Errors [Teresa Zollo]

  5. Domain/Task, Experiments • Problem-solving task • Wizard is guiding user through a campus • Wizard has detailed map • User has small fraction of map with their current surroundings • Experiments • 8 users, 8 operators, balanced male/female • 5 scenarios per user → 40 dialogs

  6. WOZ / Experimental Setting • Wizard receives recognition results on a GUI • Not parsed (user plays parser also) • Confidence denoted by color intensity • Users know they are talking to a human • Normal wizard more costly • Hard to maintain subjects for longitudinal studies • Conflicting information on change in linguistic patterns when speaking to a machine vs. to a human • Operators are naïve, they are also subjects of the study

  7. Results • 43% WER, 7.3% OOV • Manual labeling of operator understanding • Full understanding • Partial understanding • Non-understanding • Misunderstanding • Very few misunderstandings • Operators good at rejecting • Users thought they were almost always understood

  8. Results (continued) • 3 main operator strategies (approx equally distributed) for dealing with non- and partial understandings: • Continuation of route description • Signal of non-understanding • Task-related question • PARADISE-likeregression indicatesstrategy 2 isinversely correlatedwith “how well do youthink you did?”

  9. WOZ experiments • Modify the WOZ setting so that the wizard does not hear the user, but rather receives the recognition result • Exploring Human Error Handling Strategies [Gabriel Skantze] • A Study of Human Dialogue Strategies in the Presence of Speech Recognition Errors [Teresa Zollo]

  10. Domain / Experiments • TRIPS-Pacifica: planning the evacuation of the fictitious island Pacifica • Construct a plan to transport all the civilians on Pacifica to Barnacle by 5 am so that they can be evacuated from the island (the play will be deployed at midnight) • + the road between Calypso and Ocean Beach is impassable • Only 7 dialogs (September ’99)

  11. WOZ / Experimental Setting • Wizard assisted by GUI for quick information access and generating synthesized responses • Sphinx-2 (CMU), TrueTalk (Entropics) • Wizard receives string of words (paper does not mention confidence scores) • User debriefing questionnaire • Wizard annotates interaction transcript with knowledge sources used in decisions, etc…

  12. Results • Small corpus • 7 dialogs • 348 utterances • Manually labeled misunderstandings • Overall WER: 30% • Looked at positive and negative feedback

  13. Negative feedback • Request for full repetition: 33/80 • 24/33 cases users complied and repeated/rephrased • WH-replacement of missing or erroneous word: 12/80 • 8/12 cases users responded with the precise info • Attempt to salvage correct word: 20/80 • Possibly increase user satisfaction? • Similar responses to ask for repeat • Request for verification: 15/80 • 10/15 responded by explicit affirmations

  14. What if we wanted to do these? • Request for full repetition: 33/80 • 24/33 cases users complied and repeated/rephrased • WH-replacement of missing or erroneous word: 12/80 • 8/12 cases users responded with the precise info • Attempt to salvage correct word: 20/80 • Possibly increase user satisfaction? • Similar responses to ask for repeat • Request for verification: 15/80 • 10/15 responded by explicit affirmations

  15. More negative feedback results • Wizards gave negative feedback in 80 cases (35%) of the total 227 recognized incorrectly • Compensation for ASR: • Ignoring words that are not salient in the TRIPS domain • Hypothesizing correct words based on phonetic similarity • Q: So, what does that say? Better parsing?

  16. Positive feedback • Using an acknowledgement term (okay, right) • Simple response to question (next relevant contribution) • Conversational/social response i.e. greetings/thanks • Providing a next unsolicited relevant contribution • Clarifying or correcting • Paraphrasing

  17. Conclusions • Observations consistent with theoretical grounding models (Clark et al) • Negative feedback only when really needed • Unless ASR is perfect (and sometimes even then), wizards give explicit indications of their understanding

  18. Discussion… • WOZ setting… • Wizard = Parser + Dialog Manager • Seems that humans can extract more info from text than current parsers • we need better, more robust parsers? • How about Wizard = Dialog Manager? • Domain choice • Skantze results make sense in chosen domain • How can such results hold across domains?

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