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AUTOMATIC DETECTION OF REGISTER CHANGES FOR THE ANALYSIS OF DISCOURSE STRUCTURE

AUTOMATIC DETECTION OF REGISTER CHANGES FOR THE ANALYSIS OF DISCOURSE STRUCTURE. Céline De Looze celine.delooze@lpl-aix.fr. Laboratoire Parole et Langage, CNRS et Université de Provence Aix-en-Provence, France. Local vs. global pitch characteristics. → Bolinger (1951)

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AUTOMATIC DETECTION OF REGISTER CHANGES FOR THE ANALYSIS OF DISCOURSE STRUCTURE

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  1. AUTOMATIC DETECTION OF REGISTER CHANGESFOR THE ANALYSIS OF DISCOURSE STRUCTURE Céline De Looze celine.delooze@lpl-aix.fr Laboratoire Parole et Langage, CNRS et Université de Provence Aix-en-Provence, France

  2. Local vs. global pitch characteristics → Bolinger (1951) • Local: changes in the phonological representation of intonation • Global: variations in register key (level) and span (range) → Trager (1957) 2 4 1 1 Expandedspan Narrowspan 2 4 1 3 Higherkey Lowerkey

  3. Local vs. global pitch characteristics → Functional aspect of local and global pitch variations → Register variations in intonation systems • ToBI (Pierrehumbert, 1980): binary phonological distinction (H&L tones) • INTSINT (Hirst & Di Cristo, 1998): 8 possible tonal values where H & L tones are interpreted with respect to the previous tone or with respect to the speaker’s register make the crucial assumption that the speaker's key and range remain constant.

  4. Overview • ADoReVA • Topic changes as reflected by register variations • Predictingtopic changes throughautomaticdetection of register variations

  5. ADoReVA • AutomaticDetection of Register Variations Algorithm • A Praat Plugin • A clusteringalgorithm: representsthrough a binarytree structure the wayunits are groupedtogetheraccording to theirdifferences in registerkey and range • Correlationwithfunctional annotation

  6. ADoReVA Calculate Register differences… Calculates the difference between two consecutive units for keyparameter = sqrt( log2(median_unit) – log2(median_prevUnit))^2 Calculates the difference between two consecutive units for range parameter = sqrt( log2(max/min_unit) – log2(max/min_prevUnit))2 Recursively reduces the Euclidian distance between two consecutive units in a space defined by key and span parameters = sqrt( (diffkey)^2+(diffrange)^2)

  7. ADoReVA Calculate Register differences… The detection of registerkey and range isdoneafter the deletion of micro-prosodiceffectsthanks to the formulae • floor = q25*0.75 • - ceiling = q75*1.75 Which quantiles from q05 to q95 are best correlatedwithmanual annotations of pitch extrema? (De Looze & Hirst, 2007)

  8. ADoReVA To Clustering tree… The clustering algorithm groups units according to their difference in key and range. The smaller the difference between two units, the sooner these units are branched together.

  9. ADoReVA To Clustering tree… The output generated by the algorithm is a binary tree structure in the form of a layered iciclediagram Hierarchical structure

  10. ADoReVA To Clustering tree… The output generated by the algorithm is a binary tree structure in the form of a layered iciclediagram Relational Organisation

  11. ADoReVA To Clustering tree… The output generated by the algorithm is a binary tree structure in the form of a layered iciclediagram

  12. ADoReVA To Clustering tree… The output generated by the algorithm is a binary tree structure in the form of a layered iciclediagram

  13. ADoReVA Calculate Node Distances… To Stat Analyses… Calculate node distances between the leaves (or units) of the tree and correlate them (within a table) with manual annotation functions.

  14. Topic changes as reflected by register changes Are large differencesin registerbetweentwoconsecutiveunitscorrelatedwithtopic changes? Are large node distances betweentwoleavescorrelatedwithtopic changes? Topic changes

  15. Topic changes as reflected by register changes Litterature reports: • Register variations throw light on the informational organisation of the discourse structure: • →The information weightcarried out by the discourseelement • → The hierarchical dimension and relational organisation of linguistic units • High and expandedregistersignals • → Introduction of a new topic or topic change • → Discourseelementcarrying new information • → Elementsat the beginning of the utterance • → … Lehiste, 1970, Brazil, 1980; Menn & Boyce, 1982; Kutik et al, 1983; Hirschberg & Pierrehumbert 1986 ; Thorsen, 1986; Nakajima & Allen, 1992;; Sluijter & Terken, 1993; Arons, 1994; Nicolas & Hirst, 1995; Fon, 2002; Kong, 2004; Chiu-yu et al, 2005; Mayer et al, 2006; denOuden et al, 2009

  16. Topic changes as reflected by register changes Litterature reports: • Low and compressedregistersignals • → Final parts of the utterance • → Topiccontinuity • → sub-topics, parentheticalcomments • → … Lehiste, 1970, Brazil, 1980; Menn & Boyce, 1982; Kutik et al, 1983; Hirschberg & Pierrehumbert 1986 ; Thorsen, 1986; Nakajima & Allen, 1992;; Sluijter & Terken, 1993; Arons, 1994; Nicolas & Hirst, 1995; Fon, 2002; Kong, 2004; Chiu-yu et al, 2005; Mayer et al, 2006; denOuden et al, 2009

  17. Topic changes as reflected by register changes Assumption • Detection of topic changes throughdetection of large node distances • Informing about declination/ final lowering: what temporal span?

  18. Corpora • PFC Corpus : 30 minutes of read speech from 10 French-native speakers (Delais-Roussarie & Durand, 2003) • PAC Corpus: 30 minutes of read speech from 8 English-native speakers (www.pac-project.com) • CID corpus : 40 minutes of dialogue from 8 French-native speakers (Bertrand et al, 2007) • Aix-Marsec Corpus: 30 minutes of dialogue from 9 English-native speakers (Auran et al, 2004)

  19. Functional Annotation • Asimplified version of Grosz & Sidner (1986) as used in Fon (2002) and Kong (2004) • DSP2, DSP1, DSP0 between prosodic words → DSP0: no discourse boundary/ related units → DSP1: hierarchicallysuperior relation betweenunits/ but stillsharerelatedpurposes (cause-effect/ clarifyingrelationship) → DSP2: no relateddiscoursepurposes or topics

  20. PreliminaryResults • Higher and expandedregister • Large differences in key and range or Large Euclidian distances • Large node distances in the binarytree structure Correlatedwithtopic changes/ DSP2 annotation

  21. PreliminaryResults • Higher and expandedregister • Large differences in key and range or Large Euclidian distances • Large node distances in the binarytree structure Range isnot alwaysinvolved in signalingtopic changes. Not range PAC Corpus (read speech) Key: F(2, 3003) = 67.26, p-value: < 2.2e-16 Range: F(2, 3003) =0.1469, p-value = 0.8634 Both Key and Range Aix-Marsec Corpus (dialogue speech) Key: F(2, 3446)=146.3, p-val< 2.2e-16 Range: F(2, 3446)=23.98, p-val: 4.549e-11 Range lessthankey French Corpora (read and dialogue speech) Key: F(2, 2398)=142, p-val< 2.2e-16 Range: F(2, 2398)=6.233, p-val: 0.0019

  22. PreliminaryResults • Higher and expandedregister • Large differences in key and range or Large Euclidian distances • Large node distances in the binarytree structure Range isnot alwaysinvolved in signalingtopic changes. Speaking styles? Lively speech markedwith variations in range

  23. PreliminaryResults Range isnotcorrelatedwith DSP1 annotation Cause-effect/ clarifyingrelationshipbetweentwoconsecutiveunitsmaybesignaledwithmodifyingkeyonly

  24. PreliminaryResults Key appears as a stable parameterwhile range maybeoptional to indicatetopic changes Variations in range maybeseen as marking a speaker’sinvolvmentwhiletellinghis/her story Key and range parametersconveydifferentfunctions and have to bestudiedseparatly

  25. Prediction • Predictingtopic changes throughautomaticdetection of register variations • Confusion matrices: → 6 Features: key/ range differences in key/range node distances for key/range → 2 Classes: DSP0, DSP1/ DSP2

  26. Prediction Predictionwithfeatureskey/ difference in key and node distance for key → givesbetterresultsthan range, difference in range and node distances range.

  27. Prediction Predictionwithbothfeatures → key and difference in key or → key and node distance for key slightlyimprove the detection of topic changes Key feature DiffKeyfeature NodDKfeature Key & diffkey Key & NodDK

  28. Prediction Higher scores of prediction for dialogue speech thanread speech → between 20-30% predicted for read speech → about 40% predicted for dialogue speech

  29. Discussion Objective detection of register variations vs. subjective annotation of topic changes Detection of otherfunctionsthantopic changes as reflected by register variations • Detection of topic changes throughautomaticdetection of • Tempo variations (pause & speaking rate) • Intensity variations

  30. Discussion ? Usefulness of the algorithm Betterunderstanding of the hierarchical and organisational structure of discourse How do units fit together?

  31. Conclusion ADoReVA • An algorithm to understand the structure of speech as reflected by register variations Testing different units Subjective annotation vs. objective detection • An algorithm to beimplementedinto intonation systems to improve the phonologicalrepresentation of intonation • (INTSINT: Detection of Top/Mid/Bottomtakingintoaccountregister variations) A graphical representation to serve pre-analysis

  32. Merci

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