A machine learning based system for the automatic evaluation of aphasia speech
Open accessAbstract
Aphasia is an acquired language disorder resulting from damage to language related networks of the brain, most often as a result of ischemic stroke or traumatic brain injury. Within the European Union, over 580000 people are affected each year. Both assessment and treatment of aphasia require the analysis of language, in particular of spontaneous speech. Factoring in therapy and diagnosis sessions, which require the presence of a speech therapist and a physician, aphasia is a resource intensive condition: It has been estimated that in Germany alone, there are 70000 new cases of stroke-related aphasia every year, 35000 of which persist over more than six months - all of which should receive formal diagnostic testing at some point. Having an automatic system for the detection and evaluation of aphasic speech would be of great benefit for the medical domain by immensely speeding up diagnostic processes and thus freeing up valuable resources for, e.g., therapy. As a first step towards building such a system, it is necessary to identify the vocal biomarkers which characterize aphasic speech. Furthermore, a database is needed which maps from recordings of aphasic speech to the type and severity of the disorder. In this paper, we present the vocal biomarkers and a description of the existing Aachen Aphasia database containing recordings and transcriptions of therapy sessions. We outline how the biomarkers and the database could be used to construct a recognition system which automatically maps pathological speech to aphasia type and severity.
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Model-B can be retrained with properly labeled data that include all aphasia syndromes to discriminate between them.
“However, it can be retrained with properly labeled data that include all aphasia syndromes such as Global aphasia, Broca’s aphasia, Wernicke’s aphasia and amnesic aphasia datasets to discriminate between them [13].”
From Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment (2021) · cites this paper as [13]
The Abstract proposes a system that maps speech to aphasia type, and the Classifier section describes training a classifier to distinguish defined classes. The Aphasia types section names the four syndromes listed in the claim, but also identifies rarer and unclassified cases. The paper does not say that an existing Model-B can be retrained to discriminate among all aphasia syndromes. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMost research on automatic aphasia assessment has focused on a single aphasia assessment task using automatic speech recognition techniques and a machine learning framework with a fixed dataset-type.
“Most research on automatic aphasia assessment [5,9,10,12,13,14,18] has focused on a single aphasia assessment task using automatic speech recognition techniques and a machine learning (ML) framework with a fixed dataset-type.”
From Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment (2021) · cites this paper as [13]
The proposed system section describes using the Aachen database to train a machine-learning system for automatic aphasia assessment, supporting this paper as an example of that approach. But it proposes rating speech along six dimensions rather than a single assessment task, and describes acoustic-feature extraction rather than automatic speech recognition. The Conclusion also says large portions of the system remain to be implemented. (LLM, conf=0.88)
Experimental — accuracy not yet measured
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- Authors
- Kohlschein, C., Schmitt, M., Schuller, B., Jeschke, S., & Werner, C. J.
- Affiliations
- RWTH Aachen University (DE)
- University of Passau (DE)
- Universitätsklinikum Aachen (DE)
- Venue
- 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1-6
- OpenAlex
- W2772213437
- Semantic Scholar
- 1f2ce76cc1bbb128414db1db7f3b8f4cf78923ec
- Cited by
- 29
- References
- 33
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 6 · 2022: 6 · 2021: 6 · 2020: 3 · 2019: 1 · 2018: 3
- FWCI
- 12.12
- Type
- conference paper
- Language
- en
- Open access
- Locations
- IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) (publishedVersion)
- OPUS (Augsburg University) (submittedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Keywords
- aphasia, vocal biomarkers, pathological speech, automatic speech evaluation
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