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Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2023,12,19]]},"abstract":"<jats:p>Speech-based diaries from mobile phones can capture paralinguistic patterns that help detect mental illness symptoms such as suicidal ideation. However, previous studies have primarily evaluated machine learning models on a single dataset, making their performance unknown under distribution shifts. In this paper, we investigate the generalizability of speech-based suicidal ideation detection using mobile phones through cross-dataset experiments using four datasets with N=786 individuals experiencing major depressive disorder, auditory verbal hallucinations, persecutory thoughts, and students with suicidal thoughts. Our results show that machine and deep learning methods generalize poorly in many cases. Thus, we evaluate unsupervised domain adaptation (UDA) and semi-supervised domain adaptation (SSDA) to mitigate performance decreases owing to distribution shifts. While SSDA approaches showed superior performance, they are often ineffective, requiring large target datasets with limited labels for adversarial and contrastive training. Therefore, we propose sinusoidal similarity sub-sampling (S3), a method that selects optimal source subsets for the target domain by computing pair-wise scores using sinusoids. Compared to prior approaches, S3 does not use labeled target data or transform features. Fine-tuning using S3 improves the cross-dataset performance of deep models across the datasets, thus having implications in ubiquitous technology, mental health, and machine learning.<\/jats:p>","DOI":"10.1145\/3631452","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:52:04Z","timestamp":1705063924000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile Phones"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2489-1130","authenticated-orcid":false,"given":"Arvind","family":"Pillai","sequence":"first","affiliation":[{"name":"Dartmouth College, Hanover, New Hampshire, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4314-9505","authenticated-orcid":false,"given":"Subigya Kumar","family":"Nepal","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6738-9944","authenticated-orcid":false,"given":"Weichen","family":"Wang","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2369-600X","authenticated-orcid":false,"given":"Matthew","family":"Nemesure","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0866-0508","authenticated-orcid":false,"given":"Michael","family":"Heinz","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9164-4973","authenticated-orcid":false,"given":"George","family":"Price","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6995-9223","authenticated-orcid":false,"given":"Damien","family":"Lekkas","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8258-2272","authenticated-orcid":false,"given":"Amanda C.","family":"Collins","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5462-575X","authenticated-orcid":false,"given":"Tess","family":"Griffin","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2841-0493","authenticated-orcid":false,"given":"Benjamin","family":"Buck","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7771-8323","authenticated-orcid":false,"given":"Sarah Masud","family":"Preum","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0159-6697","authenticated-orcid":false,"given":"Trevor","family":"Cohen","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8832-4741","authenticated-orcid":false,"given":"Nicholas C.","family":"Jacobson","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6597-2407","authenticated-orcid":false,"given":"Dror","family":"Ben-Zeev","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7394-7682","authenticated-orcid":false,"given":"Andrew","family":"Campbell","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2632048.2632100"},{"key":"e_1_2_1_2_1","volume-title":"Youtube-8m: A large-scale video classification benchmark. arXiv preprint arXiv:1609.08675","author":"Abu-El-Haija Sami","year":"2016","unstructured":"Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan. 2016. 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In 2017 ieee international conference on acoustics, speech and signal processing (icassp). IEEE, 131--135."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.5153\/sro.1194"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2022.06.064"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1002\/0471722146"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-21905-5_9"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2020.3044503"},{"key":"e_1_2_1_51_1","volume-title":"International encyclopedia of statistical science","author":"Joyce James M","unstructured":"James M Joyce. 2011. Kullback-leibler divergence. In International encyclopedia of statistical science. 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