Emilien Gorène

Automatic processing for pathologic speech : The case of schizophrenia Under direction of Maud Champagne-Lavau and Laurent Prévot. Recording subjects/experimentator conversations Transcribing precisely with Praat Aligned on the signal by Sppas Tagging in part of speech, and graphic interface to visualize the transcriptions (souce lpl-tal-ptools S. Rauzy) Pre processing : 22 volounteers : 6 hours 488 transcriptions tokens 2 populations : Healthy Subject = Controls / Schizophrenics = Patients

According to literature : Schizophrenics’ language presents differences at multiple levels... 2 differents tasks : Describing comics and a single picture 18 points automatically detected and measured. Automatic processing for pathologic speech : The case of schizophrenia o Duration o Silent number o Number of tokens o Variety of tokens o Fluency o Lexical Richness o Number of verbs o Number of action verbs o Number of mind verbs o Possessives pronoun o Definite pronouns o …

Utilization of semi-automatic detection on pathologic corpus is justified. The only recording in a specific task may predict a psychiatric pathology. We globally find same results on 2 corpora but some indicators are in opposition. The inter-subjectal and inter-situational variations are important. Automatic processing for pathologic speech : The case of schizophrenia What can we learn ?

Exploiting the variation in conversational feedback to characterize the nature and quality of language interactions How to analyze linguistics and cyclic phenomenons evolving through time ? Can we use tools coming from biological sciences on language ? Silent/Speech/Feedback, a good categorization of convergence, else what ? Maptask-Aix ( : 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 create unambiguous categories. Under the direction of Noël Nguyen and Laurent Prévot.

After normalization, we draw representations of degrees of similarity with more or less time delay Software R with package CRQA All pairs show similar graphics : maximum of similarity on the « time delay 0 » = default position. Exploiting the variation in conversational feedback to characterize the nature and quality of language interactions

Cross-recurrence tools are useful for language sciences too. Now we can study cyclics phenomens and their evolving through time. The three categorized states haven’t consequences on results. This analysis shows systematic results for any subject : little variation Exploiting the variation in conversational feedback to characterize the nature and quality of language interactions What does this tell us ?

Thesis project 2 differents studies for the same project : The Variation can be better defined and used as an asset. We would like to use close semi-automatic method on more numerous and diversified datas to develop this idea. This is the announced goal of my thesis ! And after ?

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