The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
O Treinamento de Habilidades Sociais (SST) tem sido usado há anos para melhorar as habilidades sociais dos indivíduos para a construção de uma vida diária melhor. Nos TSS realizados por humanos, o nível de competências sociais é normalmente avaliado através de uma entrevista verbal realizada pelo formador. Embora esta avaliação se baseie no conhecimento psiquiátrico e na experiência profissional, a sua qualidade depende das capacidades do formador. Portanto, para padronizar tais avaliações, são necessárias métricas quantificáveis. Para satisfazer esta necessidade, a segunda edição da Escala de Responsividade Social (SRS-2) oferece uma solução viável porque foi extensivamente testada e padronizada por trabalhos de investigação empírica. Este artigo descreve o desenvolvimento de um método automatizado para avaliar o nível de habilidades sociais de uma pessoa com base no SRS-2. Usamos recursos multimodais, incluindo recursos baseados em BERT, e realizamos estimativa de pontuação com um coeficiente de correlação de Pearson de 0.76 ao usar a seleção de recursos. Além disso, examinamos os aspectos linguísticos dos recursos baseados em BERT através de avaliações subjetivas. Consequentemente, os recursos baseados em BERT mostram uma forte correlação negativa com pontuações subjetivas humanas de fluência, escolha apropriada de palavras e estrutura de fala compreensível.
Takeshi SAGA
Nara Institute of Science and Technology
Hiroki TANAKA
Nara Institute of Science and Technology
Hidemi IWASAKA
Nara Medical University
Satoshi NAKAMURA
Nara Institute of Science and Technology
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Takeshi SAGA, Hiroki TANAKA, Hidemi IWASAKA, Satoshi NAKAMURA, "Multimodal Prediction of Social Responsiveness Score with BERT-Based Text Features" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 3, pp. 578-586, March 2022, doi: 10.1587/transinf.2021HCP0009.
Abstract: Social Skills Training (SST) has been used for years to improve individuals' social skills toward building a better daily life. In SST carried out by humans, the social skills level is usually evaluated through a verbal interview conducted by the trainer. Although this evaluation is based on psychiatric knowledge and professional experience, its quality depends on the trainer's capabilities. Therefore, to standardize such evaluations, quantifiable metrics are required. To meet this need, the second edition of the Social Responsiveness Scale (SRS-2) offers a viable solution because it has been extensively tested and standardized by empirical research works. This paper describes the development of an automated method to evaluate a person's social skills level based on SRS-2. We use multimodal features, including BERT-based features, and perform score estimation with a 0.76 Pearson correlation coefficient while using feature selection. In addition, we examine the linguistic aspects of BERT-based features through subjective evaluations. Consequently, the BERT-based features show a strong negative correlation with human subjective scores of fluency, appropriate word choice, and understandable speech structure.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021HCP0009/_p
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@ARTICLE{e105-d_3_578,
author={Takeshi SAGA, Hiroki TANAKA, Hidemi IWASAKA, Satoshi NAKAMURA, },
journal={IEICE TRANSACTIONS on Information},
title={Multimodal Prediction of Social Responsiveness Score with BERT-Based Text Features},
year={2022},
volume={E105-D},
number={3},
pages={578-586},
abstract={Social Skills Training (SST) has been used for years to improve individuals' social skills toward building a better daily life. In SST carried out by humans, the social skills level is usually evaluated through a verbal interview conducted by the trainer. Although this evaluation is based on psychiatric knowledge and professional experience, its quality depends on the trainer's capabilities. Therefore, to standardize such evaluations, quantifiable metrics are required. To meet this need, the second edition of the Social Responsiveness Scale (SRS-2) offers a viable solution because it has been extensively tested and standardized by empirical research works. This paper describes the development of an automated method to evaluate a person's social skills level based on SRS-2. We use multimodal features, including BERT-based features, and perform score estimation with a 0.76 Pearson correlation coefficient while using feature selection. In addition, we examine the linguistic aspects of BERT-based features through subjective evaluations. Consequently, the BERT-based features show a strong negative correlation with human subjective scores of fluency, appropriate word choice, and understandable speech structure.},
keywords={},
doi={10.1587/transinf.2021HCP0009},
ISSN={1745-1361},
month={March},}
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TY - JOUR
TI - Multimodal Prediction of Social Responsiveness Score with BERT-Based Text Features
T2 - IEICE TRANSACTIONS on Information
SP - 578
EP - 586
AU - Takeshi SAGA
AU - Hiroki TANAKA
AU - Hidemi IWASAKA
AU - Satoshi NAKAMURA
PY - 2022
DO - 10.1587/transinf.2021HCP0009
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2022
AB - Social Skills Training (SST) has been used for years to improve individuals' social skills toward building a better daily life. In SST carried out by humans, the social skills level is usually evaluated through a verbal interview conducted by the trainer. Although this evaluation is based on psychiatric knowledge and professional experience, its quality depends on the trainer's capabilities. Therefore, to standardize such evaluations, quantifiable metrics are required. To meet this need, the second edition of the Social Responsiveness Scale (SRS-2) offers a viable solution because it has been extensively tested and standardized by empirical research works. This paper describes the development of an automated method to evaluate a person's social skills level based on SRS-2. We use multimodal features, including BERT-based features, and perform score estimation with a 0.76 Pearson correlation coefficient while using feature selection. In addition, we examine the linguistic aspects of BERT-based features through subjective evaluations. Consequently, the BERT-based features show a strong negative correlation with human subjective scores of fluency, appropriate word choice, and understandable speech structure.
ER -