@inproceedings{anastasia-etal-2026-multimodal,
    title = "A Multimodal Framework for Aphasia Severity Classification in {R}ussian",
    author = "Anastasia, Kolmogorova  and
      Yavshitz, Ekaterina  and
      Margolina, Anastasia  and
      Sugian, Anna",
    editor = {Danilova, Vera  and
      Kurfal{\i}, Murathan  and
      S{\"o}derfeldt, Ylva  and
      Reed, Julia  and
      Burchell, Andrew},
    booktitle = "Proceedings of the 1st Workshop on Linguistic Analysis for Health ({H}ea{L}ing 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.healing-1.22/",
    pages = "257--265",
    ISBN = "979-8-89176-367-8",
    abstract = "Automatic classification of aphasia severity presents persistent challenges, particularly for languages with limited clinical speech resources such as Russian. This paper explores a multimodal approach to severity estimation that combines acoustic and semantic representations of pathological speech. Acoustic features are extracted using pretrained Wav2Vec 2.0 models, while semantic information is obtained from the encoder of the Whisper model. The two representations are integrated via early feature fusion and evaluated using gradient boosting classifiers in a speaker-independent cross-validation setting. Experiments are conducted on a newly collected dataset of Russian speech recordings from patients with aphasia and neurotypical speakers (RuAphasiaBank). The results suggest that the combined use of acoustic and semantic embeddings can provide more stable severity estimates than unimodal baselines. This study contributes empirical evidence on the applicability of multimodal representation learning for aphasia severity classification under data-scarce conditions."
}