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Misrecognitions and Corrections in Spoken Dialogue Systems

Explore how spoken dialogue systems adapt to user corrections and system errors, and the role of prosodic cues in improving recognition accuracy. Learn about real-time interaction technology and tailored dialogue design for better user satisfaction.

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Misrecognitions and Corrections in Spoken Dialogue Systems

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  1. Misrecognitions and Corrections inSpoken Dialogue Systems Diane Litman AT&T Labs -- Research (Joint Work With Julia Hirschberg, AT&T, and Marc Swerts, IPO)

  2. Talking to a Machine….and Getting an Answer • Today’s spoken dialogue systems make it possible to accomplish real tasks, over the phone, without talking to a person • Real-time speech technology enables real-time interaction • Speech recognition and understanding is ‘good enough’ for limited, goal-directed interactions • Careful dialogue design can be tailored to capabilities of component technologies • Limited domain • Judicious use of system initiative vs. mixed initiative

  3. Some RepresentativeSpoken Dialogue Systems Deployed Brokerage (Schwab-Nuance) Mixed Initiative User E-MailAccess (myTalk) System Initiative Air Travel (UA Info-SpeechWorks) Directory Assistant (BNR) Communicator (DARPA Travel) MIT Galaxy/Jupiter Communications (Wildfire, Portico) Customer Care (HMIHY – AT&T) Banking (ANSER) ATIS (DARPA Travel) Multimodal Maps (Trains, Quickset) Train Schedule (ARISE) 1980+ 1990+ 1993+ 1995+ 1997+ 1999+

  4. But...Systems Have Trouble Knowing When They’ve Made a Mistake • Hard: correcting system misconceptions (Krahmer et al `99) User: I want to go to Boston. System: What day do you want to go to Baltimore? • Easier: answering explicit requests for confirmation or responding to ASR rejections System: Did you say you want to go to Baltimore? System: I'm sorry. I didn't understand you. Could you please repeat your utterance? • But constant confirmation or over-cautious rejection lengthens dialogue and decreases user satisfaction

  5. …And Systems Have Trouble Recognizing User Corrections • Probability of recognition failures increases after a misrecognition(Levow ‘98) • Corrections of system errors often hyperarticulated (louder, slower, more internal pauses, exaggerated pronunciation)  more ASR error (Wade et al ‘92, Oviatt et al ‘96, Swerts & Ostendorf ‘97, Levow ‘98, Bell & Gustafson ‘99)

  6. Can Prosodic Information Help Systems Perform Better? • If errors occur where speaker turns are prosodically ‘marked’…. • Can we recognize turns that will be misrecognized by examining their prosody? • Can we modify our dialogue and recognition strategies to handle corrections more appropriately?

  7. Current Study • Corpus of recognized speech collected by an interactive voice response system • Identify speaker ‘turns’ • that are incorrectly transcribed or semantically incorrect • at which speakers first become aware of a misrecognition • that correct misrecognitions • Identify prosodic features of turns in each category and compare to turns not so classified • Use Machine Learning techniques to train a classifier to make these distinctions automatically misrecognition aware site correction

  8. 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

  9. TOOT Dialogues • Collected to study effects of differences in dialogue strategy on user performance and satisfaction (Litman & Pan ‘99) • type of initiative (system, user, mixed) • type of confirmation (explicit, implicit, none) • adaptability condition • Subjects • 39 summer students • 16/23 (F/M) • 20/19 (native speaker/non) • Platform: combined over-the-phone ASR and TTS (Kamm et al ‘97) with web access to train information • Task: find train information for 4 scenarios

  10. Corpus for current study: • 152 dialogues • 2328 speaker turns (1975 with ASR hypothesis) • Misrecognitions • Overall word accuracy: 49% (WER 51%) • Overall concept accuracy(CA): 71% • “I want to go to Boston from Philadelphia” (2 domain concepts) • recognized as “I want to go to Boston” (one concept) = 50%

  11. A Successful Dialogue S: Hi, this is AT&T Amtrak schedule system. This is TOOT. How may I help you? U: I would like a train to New York City from Philadelphia on Sunday at ten thirty P M S: I heard you say “go to New York City from Philadelphia on Sunday approximately at ten thirty p.m." Do you want me to find the trains from Philadelphia to New York City on Sunday approximately at ten thirty p. m. now? U: Yes. S: I am going to get the train schedule for you....

  12. An Unsuccessful Dialogue S: Hi, this is AT&T Amtrak schedule system. This is TOOT. How may I help you? U: I'd like to find a train from Ch- to Chicago from Baltimore. S: Sorry, I can't understand you. Please repeat your utterance. U: I'd like to find a train from Baltimore to Chicago. S: I heard you say “leave today around 5 thirty in the morning ." Which city do you want to go to? U: I would like to leave Saturday at eight in the morning. S: You can say the arrival city name, such as “New York City." U: Baltimore to Chicago. S: You can say the arrival city name, such as “New York City.” U: Baltimore to Chicago. (I hate it.)

  13. Descriptive Analyses • For each speaker, for each prosodic feature, calculate mean values for e.g. all correctly recognized speaker turns and for all incorrectly recognized turns • Perform paired t-tests on these speaker pairs of means (e.g., for each speaker, pairing mean values for correctly and incorrectly recognized turns) • Similarly for corrections vs. non-corrections, aware vs. non-aware

  14. Prosodic Features Examined per Turn • Raw prosodic/acoustic features • f0 maximum and mean (pitch excursion/range) • rms maximum and mean (amplitude) • total duration • duration of preceding silence • amount of silence within turn • speaking rate (estimated from syllables of recognized string per second) • Normalized versions of each feature (compared to first turn in task, to previous turn in task, Z scores)

  15. Predicting Turn Types Using Machine Learning • Used Ripper (Cohen ‘96) to automatically induce rule sets for predicting WER and CA scores • greedy search guided by a measure of information gain • input: vectors of feature values • output: ordered rules for predicting dependent variable and (cross-validated) scores for each rule set, which identify best performers and likely performance on unseen data • Features examined: • all prosodic features, raw and normalized • original experimental conditions (adaptability of system, initiative type, style of confirmation, subject, task) • gender, native/non-native status • ASR recognized string, grammar, and acoustic confidence score

  16. 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

  17. Distinguishing Correct Recognitions from Misrecognitions (NAACL ‘00) • Misrecognitions differ prosodically from correct recognitions: • F0 maximum (higher) • RMS maximum (louder) • turn duration (longer) • preceding pause (longer) • Effect holds up across speakers and even when hyperarticulated turns are excluded • Prosodic features plus other automatically available features (acoustic confidence scores, recognized string, grammar) predict WER-based misrecognitions with ~6.5% error -- better than ~22% of acoustic confidence measures alone

  18. 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. aware site

  19. Awareness Sites • Shorter, somewhat louder, with less internal silence – compared to other turns • Poorly recognized (47% misrecognized vs. 32% of other turns) • Machine Learning experiments: • 30% baseline (not aware site) • Average error: 25.81% +/- 1.10%

  20. 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. correction

  21. Speaker Corrections: A Serious Problem for Spoken Dialogue Systems (ICSLP ‘00) • 29% of turns in our corpus are corrections • 52% of corrections are hyperarticulated but only 12% of other turns • Corrections are misrecognized at least twice as often as non-corrections (61% vs. 33%) • But corrections are no more likely to be rejected than non-corrections…. (9% vs. 8%)

  22. Prosodic Indicators of Corrections • Corrections differ from other turns prosodically: longer, louder, higher in pitch excursion, longer preceding pause, less internal silence

  23. ML Rules for Correction Prediction Baseline: 30% (not correction) norm’d prosody +non-prosody: 18.45% +/- 0.78% automatic: 21.48% +/- 0.68% • TRUE :- gram=universal, f0max>=0.96, dur>=6.55. • TRUE :- gram=universal, zeros>=0.57, asr<=-2.95. • TRUE :- gram=universal, f0max<=1.98, dur<=1.10, tempo>=1.21, zeros>=0.71. • TRUE :- dur>=0.76, asr<=-2.97, strat=UserNoConfirm. • TRUE :- dur>=1.00, dur>=2.28, ppau<=0.86. • TRUE :- rmsav>=1.11, strat=MixedImplicit, gram=cityname, f0max>=0.70. • default FALSE.

  24. When Users Correct, What Do They Do? • Correction classes: • ‘omits’ and ‘repetitions’ lead to fewer misrecognitions than ‘adds’ and ‘paraphrases’ • Turns that correct rejections are more likely to be repetitions, while turns correcting misrecognitions are more likely to be omits

  25. Corrections and System Strategies • Matches user responses: • Would you use again (1-5): SE (3.5), MI (2.6), UNC (1.7) • Satisfaction (0-40): SE (31.25), MI (24.00), UNC (22.10)

  26. Type of correction is sensitive to strategy • much more likely to exactly repeat their misrecognized utterance in a system-initiative environment • much more likely to correct by omitting information if no system confirmation than with explicit confirmation • omits used more in MixedImplicit and UserNoConfirm conditions • “Restarts” unlikely to be recognized (77% misrecognized) and skewed in distribution: • 31% of corrections are “restarts” in MI and UNC • None for SE, where initial turns well recognized • It doesn’t pay to start over!

  27. Correction Chains U: I'd like to find a train from Ch- to Chicago from Baltimore. S: Sorry, I can't understand you. Please repeat your utterance. U: I'd like to find a train from Baltimore to Chicago. …. S: You can say the arrival city name, such as “New York City." U: Baltimore to Chicago. S: You can say the arrival city name, such as “New York City.” U: Baltimore to Chicago. (I hate it.) U: Train from Baltimore ...S: ...I heard you s...U:to Chicago.... Hello? S: You can say the name of your departure city, such as "New York City." U: Baltimore. Bal-ti-more.... S: You can say... U: Baltimore.

  28. Effects of Distance from Error on User Corrections • The further a correction is from the original error, the higher in f0 (max,mean), lower in rms (max,mean), longer (sec,words), slower, with more pause preceding, and lower CA • Corrections further in chain position from the error are similar – and their word accuracy is also lower • But corrections more distant from the immediately preceding error, while similar in f0, rms, and duration, are actually more likely to be correctly recognized (WER) – why? Not a difference among strategies.

  29. Related Work • Predicting and adapting to dialogue (Litman and Pan ‘00) & utterance level misrecognitions (Levow ‘98, Smith ‘98) • Adaptation over time using reinforcement learning (Levin & Pieraccini ‘97, Satinder et al. ‘00) • Speaking style and prosody in ASR (Weintraub et al ‘96, Wang & Hirschberg ‘93, Hirose ‘97) • Hyperarticulation, corrections and misrecognitions (Wade et al ‘92, Oviatt et al ‘96, Swerts & Ostendorf ‘97, Bell & Gustafson ‘99)

  30. Current and Future Research • Generalized misrecognition results to a new corpus and recognizer (ICSLP ‘00) • Can we identify misrecognitions more accurately using correction and “aware” prediction? • Can we predict (only) awares vs. (only) corrections vs. aware/corrections vs. “normal”? • Could a speech recognizer trained on corrections recognize them better? • Can we predict “goats” vs. “sheeps” using prosody? (ASRU ‘99)

  31. Summary • Misrecognitions are prosodically different from correct recognitions (higher, louder, longer, longer preceding pause) • Corrections are prosodically different from non-corrections (higher, louder, longer, longer preceding pause, less internal silence) • Prosodic and other differences can be used to predict misrecognitions (6.5% error) and their corrections (18.5% error) • Corrections vary with strategy and distance

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