@inproceedings{tsiwah-etal-2024-semantic,
    title = "Semantic-based {NLP} techniques discriminate schizophrenia and {W}ernicke{'}s aphasia based on spontaneous speech",
    author = "Tsiwah, Frank  and
      Mayya, Anas  and
      van Cranenburgh, Andreas",
    editor = "Kokkinakis, Dimitrios  and
      Fraser, Kathleen C.  and
      Themistocleous, Charalambos K.  and
      Fors, Kristina Lundholm  and
      Tsanas, Athanasios  and
      Ohman, Fredrik",
    booktitle = "Proceedings of the Fifth Workshop on Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments @LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.rapid-1.1",
    pages = "1--8",
    abstract = "People with schizophrenia spectrum disorder (SSD){---}a psychiatric disorder, and people with Wernicke{'}s aphasia {---} an acquired neurological disorder, are both known to display semantic deficits in their spontaneous speech outputs. Very few studies directly compared the two groups on their spontaneous speech (Gerson et al., 1977; Faber et al., 1983), and no consistent results were found. Our study uses word (based on the word2vec model with moving windows across words) and sentence (transformer based-model) embeddings as features for a machine learning classification model to differentiate between the spontaneous speech of both groups. Additionally, this study uses these measures to differentiate between people with Wernicke{'}s aphasia and healthy controls. The model is able to classify patients with Wernicke{'}s aphasia and patients with SSD with a cross-validated accuracy of 81{\%}. Additionally, it is also able to classify patients with Wernicke{'}s aphasia versus healthy controls and SSD versus healthy controls with cross-validated accuracy of 93.72{\%} and 84.36{\%}, respectively. For the SSD individuals, sentence and/or discourse level features are deemed more informative by the model, whereas for the Wernicke group, only intra-sentential features are more informative. Overall, we show that NLP-based semantic measures are sensitive to identifying Wernicke{'}s aphasic and schizophrenic speech.",
}

People with schizophrenia spectrum disorder (SSD){---}a psychiatric disorder, and people with Wernicke{'}s aphasia {---} an acquired neurological disorder, are both known to display semantic deficits in their spontaneous speech outputs. Very few studies directly compared the two groups on their spontaneous speech (Gerson et al., 1977; Faber et al., 1983), and no consistent results were found. Our study uses word (based on the word2vec model with moving windows across words) and sentence (transformer based-model) embeddings as features for a machine learning classification model to differentiate between the spontaneous speech of both groups. Additionally, this study uses these measures to differentiate between people with Wernicke{'}s aphasia and healthy controls. The model is able to classify patients with Wernicke{'}s aphasia and patients with SSD with a cross-validated accuracy of 81{\%}. Additionally, it is also able to classify patients with Wernicke{'}s aphasia versus healthy controls and SSD versus healthy controls with cross-validated accuracy of 93.72{\%} and 84.36{\%}, respectively. For the SSD individuals, sentence and/or discourse level features are deemed more informative by the model, whereas for the Wernicke group, only intra-sentential features are more informative. Overall, we show that NLP-based semantic measures are sensitive to identifying Wernicke{'}s aphasic and schizophrenic speech.
Tsiwah, Frank Mayya, Anas van Cranenburgh, Andreas
Semantic-based NLP techniques discriminate schizophrenia and Wernicke's aphasia based on spontaneous speech