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Prosody in Recognition/Understanding

Prosody in Recognition/Understanding. Prosody in ASR Today. Little success in improving ASR transcription More promise in non-traditional ASR-related tasks: Improving rejection Shrinking search space Automatic segmentation Identifying ‘salient’ words Disambiguating speech/dialogue acts

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Prosody in Recognition/Understanding

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  1. Prosody in Recognition/Understanding

  2. Prosody in ASR Today • Little success in improving ASR transcription • More promise in non-traditional ASR-related tasks: • Improving rejection • Shrinking search space • Automatic segmentation • Identifying ‘salient’ words • Disambiguating speech/dialogue acts • Prosody in ASR understanding

  3. Overview • Recognizing communicative ‘problems’ • ASR errors • User corrections • Identifying speech acts • Locating topic boundaries for topic tracking and audio browsing • Recognizing speaker emotion

  4. But...Systems Have Trouble Knowing When They’ve Made a Mistake • Hard for humans to correct 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. Approach • 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 misrecognition aware site correction

  8. 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)

  9. 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 • Corpus for current study: • 2328 speaker turns • 52 dialogues • Misrecognitions • Overall word accuracy: 61% • 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%

  10. 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....

  11. 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.

  12. 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.

  13. U: Bal-ti-more.... S: You can say... U: Baltimore. S: Do you want to continue and find a new set of trains? U: Yes. S: You can say "yes" or "I am done here" to find a new set of trains. U: Yes. S: Thank you for using AT&T Amtrak train time table system. See you next time. U: I nev-

  14. Are Misrecognitions, Aware Turns, Corrections Measurably Different from Other Turns? • For each type of turn: • 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)

  15. How: 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)

  16. Distinguishing Correct Recognitions from Misrecognitions (NAACL ‘00) • Misrecognitions differ prosodically from correct recognitions in • F0 maximum (higher) • RMS maximum (louder) • turn duration (longer) • preceding pause (longer) • slower • Effect holds up across speakers and even when hyperarticulated turns are excluded

  17. Does Hyperarticulation Lead to ASR Error? • In TOOT corpus: • 24.1% of turns (perceived as) hyperarticulated • Hyperarticulated turns are recognized more poorly (59.5% WER) than non-hyperarticulated turns (32.8%) • More misrecognized turns are hyperarticulated (36.5%) than correctly recognized turns (16.0%) • But .. same results w/out hyperarticulated turns

  18. Predicting Turn Types Using Machine Learning • Ripper (Cohen ‘96) automatically induces rule sets for predicting turn types • greedy search guided by measure of information gain • input: vectors of feature values • output: ordered rules for predicting dependent variable and X-validated scores for each ruleset • Independent variables: • all prosodic features, raw and normalized • experimental conditions (initiative type, confirmation style, adaptability, subject, task) • gender, native/non-native status • ASR recognized string, grammar, and acoustic confidence score

  19. ML Results: WER-defined Misrecognition

  20. Best Rule-Set for Predicting WER Using prosody, ASR conf, ASR string, ASR grammar if (conf <= -2.85 ^ (duration >= 1.27) ^ then F if (conf <= -4.34) then F if (tempo <= .81) then F If (conf <= -4.09 then F If (conf <= -2.46 ^ str contains “help” then F If conf <= -2.47 ^ ppau >= .77 ^ tempo <= .25 then F If str contains “nope” then F If dur >= 1.71 ^ tempo <= 1.76 then F else T

  21. Analyses of Awares and Corrections • Awares: • Shorter, somewhat louder, with less internal silence – compared to other turns • Poorly recognized (49.9% misrec’d vs. 34.6%) • ML results: 30.4% baseline (!aware)/Mean error: 12.2% (+/-.61) • Corrections: • longer, louder, higher in pitch excursion, longer preceding pause, less internal silence • ML results: 30% baseline/Mean error: 21.48% +/- 0.68%

  22. User Correction Behavior • 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

  23. Per task MixedImplicit SystemExplicit UserNo Confirm Mean #turns 11.7 13.4 16.2 Mean #corr 4.6 1.3 7.1 Mean #misrec 6.4 2.8 9.4 Mean #misrec corr 3.2 0.3 4.8 Role of System Strategy 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)

  24. Future Research • Hypothesis: We can improve system recognition and error recovery by • Analyzing user input differently to identify higher level features of turns systems are likely to misrecognize -- and turns speakers produce to correct their errors • Anticipating user responses to system errors in the context of different strategies and targeting special error recovery procedures • Next: combining our predictors and an over-the-phone interface to SCANMail, our voicemail browsing and retrieval system

  25. Interpreting Speech/Dialogue Acts • What function(s) is speaker turn serving? • Same phrase can perform different speech acts • ‘Yes’: acknowledgment, acceptance, question,… • Different phrases can perform the same speech act • ‘Yes’, ‘Right’, ‘Okay’, ‘Certainly’,….. • Can prosody distinguish differences/identify commonalities? • ‘Okay’: • Contours distinguish different uses (Hockey ’91) • Contours + context distinguish different uses (Kowtko ’96)

  26. Automatic Speech/Dialogue Act Recognition • S/DA recognition important for • Turn recognition (which grammar to use when) • Turn disambiguation, e.g. S: What city do you want to go to? U1: Boston. (reply) U2: Pardon? (request for information) S: Do you want to go to Boston? U1: Boston. (confirmation) U2: Boston? (question)

  27. Current Approaches • Statistical modeling to recognize phrase boundaries and accent from acoustic evidence for Verbmobil (Nöth et al ‘99) • Prosodic boundaries provide potential DA boundaries • Most frequently accented words (salient words) in training corpus + p.o.s. improve key-word selection for identifying DAs • ACCEPT (ok, all right, marvellous, Friday, free) • SUGGEST (Monday, Friday, Thursday, Wednesday, Saturday) • Improvements in DA identification over non-prosody approaches (also cf Shriberg et al ‘98 on Switchboard, Taylor et al ‘98 on Map Task)

  28. Key features: • f0 (range better than accent or phrasing), duration, energy, rate (Shriberg et al ‘98) • But little improvement of ASR accuracy • Importance of DA coding scheme: • Some DAs more usefully disambiguated than others • Some coding schemes more disambiguable than others

  29. Prosodic Correlates of Discourse/Topic Structure • Pitch range Lehiste ’75, Brown et al ’83, Silverman ’86, Avesani & Vayra ’88, Ayers ’92, Swerts et al ’92, Grosz & Hirschberg’92, Swerts & Ostendorf ’95, Hirschberg & Nakatani ‘96 • Preceding pause Lehiste ’79, Chafe ’80, Brown et al ’83, Silverman ’86, Woodbury ’87, Avesani & Vayra ’88, Grosz & Hirschberg’92, Passoneau & Litman ’93, Hirschberg & Nakatani ‘96

  30. Rate Butterworth ’75, Lehiste ’80, Grosz & Hirschberg’92, Hirschberg & Nakatani ‘96 • Amplitude Brown et al ’83, Grosz & Hirschberg’92, Hirschberg & Nakatani ‘96 • Contour Brown et al ’83, Woodbury ’87, Swerts et al ‘92

  31. Automatic Topic Segmentation • Important for audio browsing and retrieval tasks: • Broadcast News (NIST TREC SDR track) • Topic Detection and Tracking (NIST/DARPA TDT) • Customer care call recordings, focus groups • Most relies on lexical information (Hearst ‘94, Reynar ’98, Beeferman et al ‘99)

  32. Prosodic Cues to Segmentation • Paratones: intonational paragraphs (Brown et al ’80, Nakatani & Hirschberg ’95) • Recent results (Shriberg et al ’00) show prosodic cues perform as well or better than text-based cues at topic segmentation -- and generalize better? • Goal: identify sentence and topic boundaries at ASR-defined word boundaries • Procedure: • CART decision trees provided boundary predictions • HMM combined these with lexical boundary predictions

  33. Features: • Pause at boundary (raw and normalized by speaker) • Pause at word before boundary • Normalized phone and rhyme duration • F0 (smoothed/stylized): reset, range, slope and continuity • Voice quality (halving/doubling estimates as correlates of creak or glottalization) • Speaker change, time from start of turn, # turns in conversation and gender • Topic segmentation results (BN only): • Prosody alone better than LM; combined improves significantly • Useful features: pause at boundary, f0 range, turn/no turn, gender, time in turn

  34. POP-3 Server ASR Server Email Server AUDIX Server SCANMail HUB/DB Information Extraction Server Caller Id Server IR Server Client

  35. Identifying Emotion • Human perception (Cahn ‘88, Murray & Arnott ‘93) • Automatic identification (Nöth et al ‘99)

  36. Future of Prosody in Recognition/Understanding • Finding more non-traditional aspects of recognition/understanding where prosody can be useful • Finding better ways to map linguistic information (e.g. accent) into objective acoustic measures • Finding applications where prosodic information makes a difference

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