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Stochastic Analysis of Natural Conversation Corpora, with automatic detection of speech details

Stochastic Analysis of Natural Conversation Corpora, with automatic detection of speech details. Emilien Gorène. Automatic processing for pathologic speech : The case of schizophrenia. Pre processing :.

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Stochastic Analysis of Natural Conversation Corpora, with automatic detection of speech details

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  1. Stochastic Analysis of Natural Conversation Corpora, with automatic detection of speech details Emilien Gorène

  2. Automaticprocessing for pathologic speech : The case of schizophrenia Preprocessing : • 2 populations : HealthySubject = Controls / Schizophrenics = Patients • 22 volounteers : 6 hours • 488 transcriptions • 35.000 tokens • Recordingsubjects/experimentator conversations • TranscribingpreciselywithPraat • Alignedon the signal by Sppas Under direction of Maud Champagne-Lavau and Laurent Prévot. Tagging in part of speech, and graphic interface to visualize the transcriptions (soucelpl-tal-ptools S. Rauzy)

  3. Automatic processing for pathologic speech : The case of schizophrenia • According to literature : Schizophrenics’ languagepresentsdifferencesat multiple levels... • 2 differentstasks : Describingcomics and a single picture • 18 points automaticallydetected and measured. • Duration • Silentnumber • Number of tokens • Variety of tokens • Fluency • Lexical Richness • Number of verbs • Number of action verbs • Number of mindverbs • Possessives pronoun • Definitepronouns • …

  4. Whatcanwelearn ? Automaticprocessing for pathologic speech : The case of schizophrenia • Utilizationof semi-automaticdetection on pathologic corpus isjustified. • The onlyrecordingin a specifictaskmaypredict a psychiatricpathology. • Wegloballyfindsameresultson 2 corporabut someindicators are in opposition. • The inter-subjectal and inter-situational variations are important.

  5. Under the direction of Noël Nguyen and Laurent Prévot. Exploiting the variation in conversational feedback to characterize the nature and quality of language interactions How to analyzelinguistics and cyclicphenomenonsevolvingthrough time ? Can we use toolscomingfrombiological sciences on language ? Silent/Speech/Feedback, a good categorization of convergence, elsewhat ? Maptask-Aix (http://sldr.org/wiki/sldr000732) : 3h30 of recording, 300+ files… • Two speakers in interraction to retrace the good way on a map. • One is the Giver, the other the follower Categorization in 3 : speech, silent, feedback to createunambiguouscategories.

  6. Exploiting the variation in conversational feedback to characterize the nature and quality of language interactions • Afternormalization, wedrawrepresentations of degrees of similaritywith more or less time delay Software R with package CRQA • All pairs show similargraphics : maximum of similarityon the « time delay 0 » = default position.

  7. Exploiting the variation in conversational feedback to characterize the nature and quality of language interactions Whatdoesthis tell us ? • Cross-recurrencetools are useful for language sciences too. • Nowwecanstudycyclicsphenomensand theirevolvingthrough time. • The threecategorized states haven’tconsequences on results. • This analysis shows systematicresults for anysubject : little variation

  8. And after? Thesisproject • 2 differentsstudies for the sameproject : The Variation canbebetterdefined and used as an asset. • Wewouldlike to use close semi-automaticmethod on more numerous and diversified datas to developthisidea. • This is the announced goal of mythesis !

  9. Stochastic Analysis of Natural Conversation Corpora, with automatic detection of speech details Thankyou for your attention.

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