Speech coherence in schizophrenia
Schizophrenia is a severe mental disorder that is characterized by considerable aberrations of thought and perception, such as auditory hallucinations, delusions of control and thought insertion, as well as thought disorder and inappropriate affect. One of the crucial diagnostic symptoms of schizophrenia is disorganized speech. However, there is no robust and agreed upon method of assessing speech fragmentation, especially so for Russian language.
Speech of those diagnosed with schizophrenia is known to differ from the speech produced by neurotypical controls across levels of language structure: from length and patterns of pauses to frequency of usage of ambiguous references and cataphore. Our research project is dedicated to discourse coherence, that is thematic proximity of adjacent utterances (local coherence) and relation of each utterance to the overall topic of a given text (global coherence).
One of the most important topics in recent psychiatric linguistics research is automatization of coherence assessment. Most works in this area utilize vector semantics or word embeddings. Vector semantics is a way of representing words with vectors in a multidimensional space based on their co-occurrence in a large training corpus. This method allows for mathematical approximation of distributional (and, supposedly, semantic) similarity of two words or two groups of words. This technique is very well-suited for automation of local and global coherence assessment.
The participants are asked to retell the plot of the Pear film. The retelling is recorded, transcribed and manually assessed for local and global coherence using traditional scoring techniques. Then each text is analyzed using a program developed by our team. The aim is to approximate manual coherence scores by automated ones. Despite the absence of a significant difference between clinical and control groups in coherence scores on our current sample, the scores produced by the program strongly correlate with manual scores for most coherence measures used in our study.
One of the main goals of the project at the moment is adapting the techniques of assessing speech incoherence developed for English language to Russian material.
Ryazanskaya, G., & Khudyakova, M. (2018). Analysis of Discourse Macrostructure in Schizophrenia: A Corpus Study. In Neurobiology of Speech and Language (pp. 35-36).
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