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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2406.10272 |
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| _version_ | 1866914838828023808 |
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| author | Luz, Saturnino Garcia, Sofia De La Fuente Haider, Fasih Fromm, Davida MacWhinney, Brian Lanzi, Alyssa Chang, Ya-Ning Chou, Chia-Ju Liu, Yi-Chien |
| author_facet | Luz, Saturnino Garcia, Sofia De La Fuente Haider, Fasih Fromm, Davida MacWhinney, Brian Lanzi, Alyssa Chang, Ya-Ning Chou, Chia-Ju Liu, Yi-Chien |
| contents | We present a novel benchmark dataset and prediction tasks for investigating approaches to assess cognitive function through analysis of connected speech. The dataset consists of speech samples and clinical information for speakers of Mandarin Chinese and English with different levels of cognitive impairment as well as individuals with normal cognition. These data have been carefully matched by age and sex by propensity score analysis to ensure balance and representativity in model training. The prediction tasks encompass mild cognitive impairment diagnosis and cognitive test score prediction. This framework was designed to encourage the development of approaches to speech-based cognitive assessment which generalise across languages. We illustrate it by presenting baseline prediction models that employ language-agnostic and comparable features for diagnosis and cognitive test score prediction. The models achieved unweighted average recall was 59.2% in diagnosis, and root mean squared error of 2.89 in score prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10272 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Connected Speech-Based Cognitive Assessment in Chinese and English Luz, Saturnino Garcia, Sofia De La Fuente Haider, Fasih Fromm, Davida MacWhinney, Brian Lanzi, Alyssa Chang, Ya-Ning Chou, Chia-Ju Liu, Yi-Chien Computation and Language Machine Learning Sound Audio and Speech Processing J.3; I.5.4 We present a novel benchmark dataset and prediction tasks for investigating approaches to assess cognitive function through analysis of connected speech. The dataset consists of speech samples and clinical information for speakers of Mandarin Chinese and English with different levels of cognitive impairment as well as individuals with normal cognition. These data have been carefully matched by age and sex by propensity score analysis to ensure balance and representativity in model training. The prediction tasks encompass mild cognitive impairment diagnosis and cognitive test score prediction. This framework was designed to encourage the development of approaches to speech-based cognitive assessment which generalise across languages. We illustrate it by presenting baseline prediction models that employ language-agnostic and comparable features for diagnosis and cognitive test score prediction. The models achieved unweighted average recall was 59.2% in diagnosis, and root mean squared error of 2.89 in score prediction. |
| title | Connected Speech-Based Cognitive Assessment in Chinese and English |
| topic | Computation and Language Machine Learning Sound Audio and Speech Processing J.3; I.5.4 |
| url | https://arxiv.org/abs/2406.10272 |