Speech as a Biomarker for Disease Detection
Speech is a rich biomarker that encodes substantial information about the health of a speaker, and thus it has been proposed for the detection of numerous diseases, achieving promising results. However, questions remain about what the models trained for the automatic detection of these diseases are...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10767227/ |
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| author | Catarina Botelho Alberto Abad Tanja Schultz Isabel Trancoso |
| author_facet | Catarina Botelho Alberto Abad Tanja Schultz Isabel Trancoso |
| author_sort | Catarina Botelho |
| collection | DOAJ |
| description | Speech is a rich biomarker that encodes substantial information about the health of a speaker, and thus it has been proposed for the detection of numerous diseases, achieving promising results. However, questions remain about what the models trained for the automatic detection of these diseases are actually learning and the basis for their predictions, which can significantly impact patients’ lives. This work advocates for an interpretable health model, suitable for detecting several diseases, motivated by the observation that speech-affecting disorders often have overlapping effects on speech signals. A framework is presented that first defines “reference speech” and then leverages this definition for disease detection. Reference speech is characterized through reference intervals, i.e., the typical values of clinically meaningful acoustic and linguistic features derived from a reference population. This novel approach in the field of speech as a biomarker is inspired by the use of reference intervals in clinical laboratory science. Deviations of new speakers from this reference model are quantified and used as input to detect Alzheimer’s and Parkinson’s disease. The classification strategy explored is based on Neural Additive Models, a type of glass-box neural network, which enables interpretability. The proposed framework for reference speech characterization and disease detection is designed to support the medical community by providing clinically meaningful explanations that can serve as a valuable second opinion. |
| format | Article |
| id | doaj-art-e65e34229c864ad4a9ffd1fde5d7afc7 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e65e34229c864ad4a9ffd1fde5d7afc72025-08-20T01:59:12ZengIEEEIEEE Access2169-35362024-01-011218448718450810.1109/ACCESS.2024.350643310767227Speech as a Biomarker for Disease DetectionCatarina Botelho0https://orcid.org/0000-0003-4794-1003Alberto Abad1https://orcid.org/0000-0003-2122-5148Tanja Schultz2https://orcid.org/0000-0002-9809-7028Isabel Trancoso3https://orcid.org/0000-0001-5874-6313INESC-ID, Lisbon, PortugalINESC-ID, Lisbon, PortugalCognitive Systems Laboratory (CSL), University of Bremen, Bremen, GermanyINESC-ID, Lisbon, PortugalSpeech is a rich biomarker that encodes substantial information about the health of a speaker, and thus it has been proposed for the detection of numerous diseases, achieving promising results. However, questions remain about what the models trained for the automatic detection of these diseases are actually learning and the basis for their predictions, which can significantly impact patients’ lives. This work advocates for an interpretable health model, suitable for detecting several diseases, motivated by the observation that speech-affecting disorders often have overlapping effects on speech signals. A framework is presented that first defines “reference speech” and then leverages this definition for disease detection. Reference speech is characterized through reference intervals, i.e., the typical values of clinically meaningful acoustic and linguistic features derived from a reference population. This novel approach in the field of speech as a biomarker is inspired by the use of reference intervals in clinical laboratory science. Deviations of new speakers from this reference model are quantified and used as input to detect Alzheimer’s and Parkinson’s disease. The classification strategy explored is based on Neural Additive Models, a type of glass-box neural network, which enables interpretability. The proposed framework for reference speech characterization and disease detection is designed to support the medical community by providing clinically meaningful explanations that can serve as a valuable second opinion.https://ieeexplore.ieee.org/document/10767227/Alzheimer’s diseaseautomatic disease detectioninterpretabilityneural additive modelsParkinson’s diseasereference intervals |
| spellingShingle | Catarina Botelho Alberto Abad Tanja Schultz Isabel Trancoso Speech as a Biomarker for Disease Detection IEEE Access Alzheimer’s disease automatic disease detection interpretability neural additive models Parkinson’s disease reference intervals |
| title | Speech as a Biomarker for Disease Detection |
| title_full | Speech as a Biomarker for Disease Detection |
| title_fullStr | Speech as a Biomarker for Disease Detection |
| title_full_unstemmed | Speech as a Biomarker for Disease Detection |
| title_short | Speech as a Biomarker for Disease Detection |
| title_sort | speech as a biomarker for disease detection |
| topic | Alzheimer’s disease automatic disease detection interpretability neural additive models Parkinson’s disease reference intervals |
| url | https://ieeexplore.ieee.org/document/10767227/ |
| work_keys_str_mv | AT catarinabotelho speechasabiomarkerfordiseasedetection AT albertoabad speechasabiomarkerfordiseasedetection AT tanjaschultz speechasabiomarkerfordiseasedetection AT isabeltrancoso speechasabiomarkerfordiseasedetection |