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Characterizing and Recognizing Spoken Corrections in Human-Computer Dialog Gina-Anne Levow August 25, 1998

Characterizing and Recognizing Spoken Corrections in Human-Computer Dialog Gina-Anne Levow August 25, 1998. Error Correction Spiral. U: Give me the price for AT&T. S: Hewlett-Packard was 83 ¾, up 2 ½ since yesterday. U: Give me the price for AT&T. S: Data General was at 10 ½ up a quarter.

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Characterizing and Recognizing Spoken Corrections in Human-Computer Dialog Gina-Anne Levow August 25, 1998

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  1. Characterizing and RecognizingSpoken Corrections inHuman-Computer DialogGina-Anne LevowAugust 25, 1998

  2. Error Correction Spiral • U: Give me the price for AT&T. • S: Hewlett-Packard was 83 ¾, up 2 ½ since yesterday. • U: Give me the price for AT&T. • S: Data General was at 10 ½ up a quarter. • U: Give me the price for AT&T. • S: Hewlett-Packard was 83 ¾, up 2 ½. • U: Give me the price for American Telephone and Telegraph. • S: Excuse me?

  3. Identifying Corrections • Most "Reasoning" Approaches • Correction => Violates Task, Belief Constraint • Requires Tight Task, Belief Model • Often Requires Accurate Recognition • This Approach • Uses Acoustic or Lexical Information • Content, Context Independent

  4. Accomplishments • Corrections vs Original Inputs • Significant Differences: Duration, Pause, Pitch • Corrections vs Recognizer Models • Contrasts: Phonology and Duration • Correction Recognition • Decision Tree Classifier: 65-77% accuracy • Human Baseline ~80%

  5. Why Corrections? • Recognizer Error Rates ~25% • REAL meaning of utterance • user intent • Corrections misrecognized 2.5X as often • Hard to Correct => Poor Quality System

  6. Why it's Necessary • Error Repair Requires Detection • Errors can be very difficult to detect • E.g. Misrecognitions • Focus Repair Efforts • Corrections Decrease Recognition Accuracy • Adaptation Requires Identification

  7. Why is it Hard? • Recognition Failures and Errors • Repetition <> Correction • 500 Strings => 6700 Instances (80%) • Speech Recognition Technology • Variation - Undesirable, Suppressed

  8. Corrections Within

  9. Roadmap • Data Collection and Description • SpeechActs System & Field Trial • Characterizing Corrections • Original-Repeat Pair Data Analysis • Acoustic and Phonological Measures & Results • Recognizing Corrections • Conclusions and Future Work

  10. SpeechActs System • Speech-Only System over the Telephone • Access to Common Desktop Applications • Email, Calendar, Weather, Stock Quotes • BBN's Hark Speech Recognition, Centigram TruVoice Speech Synthesis • In-house: Natural Language Analysis Back-end Applications, Dialog Manager

  11. Example Interaction • SpeechActs: You have 14 new messages and 47 old messages. The first new message is from Eric Baatz regarding "report completed". • User: Let me hear it. • SpeechActs: "The first draft is ready for your comments." • User: Reply and Include the current message. • SpeechActs: Preparing message to Eric Baatz. Begin recording after the tone. When finished, pause for several seconds. • User: Eric, I'll get those to you this afternoon. • SpeechActs: Say cancel, send or review.

  12. Example Interaction: Con't • User: Send. • SpeechActs: Message Sent. What now? • User: Next message. • SpeechActs: New Message two from Stuart Adams, subject "Can we meet at 3:00 today?" • User: Switch to Calendar.

  13. System Data Overview • Approximately 60 hours of interactions • Digitized at 8kHz, 8-bit mu-law encoding • 18 subjects: 14 novices, 4 experts, single shots • 7529 user utterances, 1961 errors ~25% error • P(error | correct) = 18%; P(error | error) = 44%

  14. System: Recognition Error Types • Rejection Errors - Below Recognition Level • U: Switch to Weather • S (Heard): <nothing> • S (said): Huh? • Misrecognition Errors - Substitution in Text • U: Switch to Weather • S (Heard): Switch to Calendar • S (Said): On Tuesday August 25, you have defense • 1250 Rejections ~2/3 • 706 Misrecognitions ~1/3

  15. Roadmap • Data Collection and Description • SpeechActs System & Field Trial • Characterizing Corrections • Original-Repeat Pair Data Analysis • Acoustic and Phonological Measures & Results • Divergence from Recognizer Models • Recognizing Corrections • Conclusions and Future Work

  16. Analysis: Data • 300 Original Input-Repeat Correction Pairs • Lexically Matched, Same Speaker • Example: • S: (Said): Please say mail, calendar, weather. • U: Switch to Weather. Original • S (Said): Huh? • U: Switch to Weather. Repeat.

  17. Analysis: Duration • Automatic Forced Alignment, Hand-Edited • Total: Speech Onset to End of Utterance • Speech: Total - Internal Silence • Contrasts: Original Input/Repeat Correction • Total: Increases 12.5% on average • Speech: Increases 9% on average

  18. Analysis: Pause • Utterance Internal Silence > 10ms • Not Preceding Unvoiced Stops(t), Affricates(ch) • Contrasts: Original Input/Repeat Correction • Absolute: 46% Increase • Ratio of Silence to Total Duration: 58% Increase

  19. Pitch Tracks

  20. Analysis: Pitch I • ESPS/Waves+ Pitch Tracker, Hand-Edited • Normalized Per-Subject: • (Value-Subject Mean) / (Subject Std Dev) • Pitch Maximum, Minimum, Range • Whole Utterance & Last Word • Contrasts: Original Input/Repeat Correction • Significant Decrease in Pitch Minimum • Whole Utterance & Last Word

  21. Analysis: Pitch II

  22. Analysis: Pitch III • Internal Pitch Contours: Pitch Accent • Steepest Rise, Steepest Fall, Slope Sum • Overall => Not Significant • Misrecognitions Only: Original vs Repeat • Significant Increases: Steepest Rise, Slope Sum

  23. Pitch Contour Detail • Exclude Boundary Tone Region • 5-Point median smoothing (Taylor 1996) • Piecewise linear contour between max and min

  24. Analysis: Overview • Significant Differences: Original/Correction • Duration & Pause • Significant Increases: Original vs Correction • Pitch • Significant Decrease in Pitch Minimum • Increase in Final Falling Contours • Misrecognitions: Increase in Pitch Variability • Conversational-to-Clear Speech Shift • Contrastive Use of Pitch Accent

  25. Roadmap • Data Collection and Description • SpeechActs System & Field Trial • Characterizing Corrections • Original-Repeat Pair Data Analysis • Acoustic and Phonological Measures & Results • Divergence from Recognizer Models • Recognizing Corrections • Conclusions and Future Work

  26. Analysis: Phonology • Reduced Form => Citation Form • Schwa to unreduced vowel (~20) • E.g. Switch t' mail => Switch to mail. • Unreleased or Flapped 't' => Released 't' (~50) • E.g. Read message tweny => Read message twenty • Citation Form => Hyperclear Form • Vowel or Syllabic Insertion (~20) • E.g. Goodbye => Goodba-aye

  27. Analysis: Overview II • Original vs Correction & Recognizer Model • Phonology • Reduced Form => Citation Form => Hyperclear Form • Conversational to (Hyper) Clear Shift • Duration • Contrast between Final and Non-final Words • Departure from ASR Model • Increase for Corrections, especially Final Words

  28. Roadmap • Data Collection and Description • SpeechActs System & Field Trial • Characterizing Corrections • Original-Repeat Pair Data Analysis • Acoustic and Phonological Measures & Results • Divergence from Recognizer Models • Recognizing Corrections • Conclusions and Future Work

  29. Learning Method Options • (K)-Nearest Neighbor • Need Commensurable Attribute Values • Sensitive to Irrelevant Attributes • Labeling Speed - Training Set Size • Neural Nets • Hard to Interpret • Can Require More Computation & Training Data • +Fast, Accurate when Trained • Decision Trees • Intelligible, Robust to Irrelevant Attributes • +Fast, Compact when Trained • ?Rectangular Decision Boundaries, Don't Test Feature Combinations • Alternative: Mixture of Experts

  30. Learning Method Options • (K)-Nearest Neighbor • Need Commensurable Attribute Values • Sensitive to Irrelevant Attributes • Labeling Speed - Training Set Size • Neural Nets • Hard to Interpret • Can Require More Computation & Training Data • +Fast, Accurate when Trained • Decision Trees <= • Intelligible, Robust to Irrelevant Attributes • +Fast, Compact when Trained • ?Rectangular Decision Boundaries, Don't Test Feature Combinations

  31. Amplitude Max, Mean, Last Max-Last (ampdiff) Mean-Last (ampdelta) Pitch Max, Min, Range Global, Last Word Range/Total Contour Max, min, sum slope Decision Tree Features • 38 Features Total, E.g. • 15 for best trees • Pause • Total Pause Duration • Pause / Total Duration • Duration • Total Duration (uttdur) • Speaking Rate (sps) • Normalized Duration

  32. Decision Tree Training & Testing • Data: 50% Original Inputs, 50% Repeat Corrections • Classifier Labels: Original, Correction • 7-Way Cross-Validation • Train on 6/7 of data, Test on remaining 1/7 • Subsets drawn at random according to distribution • Cycle through all subsets, training & testing • Report average results on unseen test data

  33. Recognizer: Results (Overall) • Tree Size: 57 (unpruned), 37 (pruned) • Minimum of 10 nodes per branch required • First Split: Normalized Duration (All Trees) • Most Important Features: • Normalized & Absolute Duration, Speaking Rate • 65% Accuracy - Null Baseline-50%

  34. Example Tree

  35. Classifier Results: Misrecognitions • Most important features: • Absolute and Normalized Duration • Pitch Minimum and Pitch Slope • 77% accuracy (with text) • 65% (acoustic features only) • Null baseline - 50% • Human baseline - 79.4% (Hauptman & Rudnicky 1990)

  36. Classifier Results: Misrecognitions • Most important features: • Absolute and Normalized Duration • Pitch Minimum and Pitch Slope • 77% accuracy (with text) • 65% (acoustic features only) • Errors, most trees: ½ false positive, ½ false negative • Null baseline - 50% • Human baseline - 79.4% (Hauptman & Rudnicky 1990)

  37. Misrecognition Classifier

  38. Roadmap • Data Collection and Description • Characterizing Corrections • Recognizing Corrections • Conclusions and Future Work

  39. Accomplishments • Contrasts between Originals vs Corrections • Significant Differences in Duration, Pause, Pitch • Conversational-to-Clear Speech Shifts • Shifts away from Recognizer Models • Corrections Recognized at 65-77% • Near-human Levels

  40. The Recipe • Original/Correction Training Set (300+ sets) • Labeled, Transcribed, Digitized, Corpus or Wizard • Acoustic Analyses • Pitch Tracking, Silence Detection, Speaking Rate,... • Classifier Training & Tuning • Confidence Measure (Weighted Pessimistic Error) • Phonological Rule Extraction • Durational Contrast Modeling • Repair Dialog Management

  41. Future Work • Modify ASR Duration Model for Correction • Reflect Phonological and Duration Change • Identify Locus of Correction for Misrecognitions • Preliminary tests: • 26/28 Corrected Words Detected, 2 False Alarms

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