Disentangled Latent Speech Representation for Automatic Pathological Intelligibility Assessment

Weise T, Maier A, Nöth E, Heismann B, Schuster M, Yang SH (2022)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2022

Event location: Songdo KR

Abstract

Speech intelligibility assessment plays an important role in the therapy of patients suffering from pathological speech disorders. Automatic and objective measures are desirable to assist therapists in their traditionally subjective and labor-intensive assessments. In this work, we investigate a novel approach for obtaining such a measure using the divergence in disentangled latent speech representations of a parallel utterance pair, obtained from a healthy reference and a pathological speaker. Experiments on an English database of Cerebral Palsy patients, using all available utterances per speaker, show high and significant correlation values (R = −0.9) with subjective intelligibility measures, while having only minimal deviation (±0.01) across four different reference speaker pairs. We also demonstrate the robustness of the proposed method (R = −0.89 deviating ±0.02 over 1000 iterations) by considering a significantly smaller amount of utterances per speaker. Our results are among the first to show that disentangled speech representations can be used for automatic pathological speech intelligibility assessment, resulting in a reference speaker pair invariant method, applicable in scenarios with only few utterances available.

Authors with CRIS profile

Tobias Weise Professur für Artificial Intelligence in Biomedical Speech Processing (Stiftungsprofessur) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Elmar Nöth Professur für Informatik (Mustererkennung) Seung Hee Yang Professur für Artificial Intelligence in Biomedical Speech Processing (Stiftungsprofessur)

Involved external institutions

Siemens AG, Healthcare Sector DE Germany (DE) Ludwig-Maximilians-Universität (LMU) DE Germany (DE)

How to cite

APA:

Weise, T., Maier, A., Nöth, E., Heismann, B., Schuster, M., & Yang, S.H. (2022). Disentangled Latent Speech Representation for Automatic Pathological Intelligibility Assessment. In Proceedings of the Proceedings of INTERSPEECH 2022. Songdo, KR.

MLA:

Weise, Tobias, et al. "Disentangled Latent Speech Representation for Automatic Pathological Intelligibility Assessment." Proceedings of the Proceedings of INTERSPEECH 2022, Songdo 2022.

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