79,239 research outputs found

    Microphone-independent speech features for automatic depression detection using recurrent neural network

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    Depression is a common mental disorder that has a negative impact on individuals, society, and the economy. Traditional clinical diagnosis methods are subjective and necessitate extensive expert participation. Because it is fast, convenient, and non-invasive, automatic depression detection using speech signals is a promising depression objective biomarker. Acoustic feature extraction is one of the most challenging techniques for speech analysis applications in mobile phones. The values of the extracted acoustic features are significantly influenced by adverse environmental noises, a wide range of microphone specifications, and various types of recording software. This study identified microphone-independent acoustic features and utilized them in developing an end-to-end recurrent neural network model to classify depression from Bahasa Malaysia speech. The dataset includes 110 female participants. Patient Health Questionnaire 9, Malay Beck Depression Inventory-II, and subjectsโ€™ declaration of Major Depressive Disorder diagnosis by a trained clinician were used to determine depression status. Multiple combinations of speech types were compared and discussed. Robust acoustic features derived from female spontaneous speech achieved an accuracy of 85%

    Detection of Verbal and Nonverbal speech features as markers of Depression: results of manual analysis and automatic classification

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    The present PhD project was the result of a multidisciplinary work involving psychiatrists, computing scientists, social signal processing experts and psychology students with the aim to analyse verbal and nonverbal behaviour in patients affected by Depression. Collaborations with several Clinical Health Centers were established for the collection of a group of patients suffering from depressive disorders. Moreover, a group of healthy controls was collected as well. A collaboration with the School of Computing Science of Glasgow University was established with the aim to analysed the collected data. Depression was selected for this study because is one of the most common mental disorder in the world (World Health Organization, 2017) associated with half of all suicides (Lecrubier, 2000). It requires prolonged and expensive medical treatments resulting into a significant burden for both patients and society (Olesen et al., 2012). The use of objective and reliable measurements of depressive symptoms can support the clinicians during the diagnosis reducing the risk of subjective biases and disorder misclassification (see discussion in Chapter 1) and doing the diagnosis in a quick and non-invasive way. Given this, the present PhD project proposes the investigation of verbal (i.e. speech content) and nonverbal (i.e. paralingiuistic features) behaviour in depressed patients to find several speech parameters that can be objective markers of depressive symptoms. The verbal and nonverbal behaviour are investigated through two kind of speech tasks: reading and spontaneous speech. Both manual features extraction and automatic classification approaches are used for this purpose. Differences between acute and remitted patients for prosodic and verbal features have been investigated as well. In addition, unlike other literature studies, in this project differences between subjects with and without Early Maladaptive Schema (EMS: Young et al., 2003) independently from the depressive symptoms, have been investigated with respect to both verbal and nonverbal behaviour. The proposed analysis shows that patients differ from healthy subjects for several verbal and nonverbal features. Moreover, using both reading and spontaneous speech, it is possible to automatically detect Depression with a good accuracy level (from 68 to 76%). These results demonstrate that the investigation of speech features can be a useful instrument, in addition to the current self-reports and clinical interviews, for helping the diagnosis of depressive disorders. Contrary to what was expected, patients in acute and remitted phase do not report differences regarding the nonverbal features and only few differences emerges for the verbal behaviour. At the same way, the automatic classification using paralinguistic features does not work well for the discrimination of subjects with and without EMS and only few differences between them have been found for the verbal behaviour. Possible explanations and limitations of these results will be discussed

    Towards an artificial therapy assistant: Measuring excessive stress from speech

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    The measurement of (excessive) stress is still a challenging endeavor. Most tools rely on either introspection or expert opinion and are, therefore, often less reliable or a burden on the patient. An objective method could relieve these problems and, consequently, assist diagnostics. Speech was considered an excellent candidate for an objective, unobtrusive measure of emotion. True stress was successfully induced, using two storytelling\ud sessions performed by 25 patients suffering from a stress disorder. When reading either a happy or a sad story, different stress levels were reported using the Subjective Unit of Distress (SUD). A linear regression model consisting of the high-frequency energy, pitch, and zero crossings of the speech signal was able to explain 70% of the variance in the subjectively reported stress. The results demonstrate the feasibility of an objective measurement of stress in speech. As such, the foundation for an Artificial Therapeutic Agent is laid, capable of assisting therapists through an objective measurement of experienced stress

    The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level

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    Depression is a serious medical condition that is suffered by a large number of people around the world. It significantly affects the way one feels, causing a persistent lowering of mood. In this paper, we propose a novel attention-based deep neural network which facilitates the fusion of various modalities. We use this network to regress the depression level. Acoustic, text and visual modalities have been used to train our proposed network. Various experiments have been carried out on the benchmark dataset, namely, Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we empirically justify that the fusion of all three modalities helps in giving the most accurate estimation of depression level. Our proposed approach outperforms the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on mean absolute error (MAE).Comment: 10 pages including references, 2 figure

    A study on artificial intelligence-based clinical decision support system to evaluate depression and suicide risk using voice and text

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2022.2. ์•ˆ์šฉ๋ฏผ.Introduction: The incidence of depression and suicide continues to increase worldwide, and the resulting socio-economic loss is also enormous. However, in diagnosing depression and suicide, it is difficult to effectively intervene due to a misdiagnosis in situations where it is difficult for the patient to report symptoms with reduced symptoms or conduct an in-depth interview because the diagnosis must be made through the patient's subjective answer. In addition, voices and text used by participants in interviews are traditionally known for their clinical significance in the Department of Psychiatry. In the past, the voice and the subject's utterance content had to be used for diagnosis based on the clinical experience accumulated by clinicians. As technologies for extracting various indicators of voice and extracting spoken words have been developed recently, differences in voices and differences in spoken words according to the risk of depression and suicide are being revealed. In addition, as machine learning is applied to the medical field, it has been able to detect these subtle differences and help make decisions. However, previous studies have limitations in that the number of subjects is not yet sufficient and consideration of various clinical situations such as drugs is insufficient. Therefore, this study aims to overcome the existing limitations and build an AI-based clinical decision support system that evaluates depression and suicide risk groups based on the subject's voice and words used during the interview. Method: A patient group complaining of depressive symptoms and a normal control group were recruited, respectively, and a Mini International Neuropsychiatric Interview was performed on all subjects and the interview was recorded. After extracting only the section uttered by the subject from among the recorded interview files, various indicators of voice and text data were extracted from each section. In Study I, the initial data were used to compare and analyze the normal group, the mild depression group, and the major depression group. In Study II, the final initial evaluation data was used to identify voice indicators that can distinguish normal and depression, and a diagnostic algorithm using text was constructed. In Study III, an algorithm for diagnosing the high-risk group for suicide was established by defining the high-risk group for depression through the Beckโ€™s suicide ideation scale and the suicide-related module of the mini international neuropsychiatric interview. Results: In Study I, 7 voice and speech indicators that can distinguish 33 normal group, 26 mild depression group, and 34 major depression group were extracted. When the accuracy of various models was confirmed based on the voice and speech features, it was confirmed that the performance of the multilayer perceptron was the best. In Study II, the speech features and text data of 83 normal and 83 depressed patients were analyzed, and the area under the curve of the machine learning algorithm based on each was 0.806 and 0.905. In Study III, 83 people in the depression group were compared with both the Beckโ€™s suicide ideation scale and the classification method through a mini international neuropsychiatric interview. However, in the case of the algorithm for predicting the suicide high risk group based on voice, the maximum sensitivity was 0.535, and the best performance was the average accuracy of 0.495 in the model using the logistic regression formula. On the other hand, the algorithm for predicting suicide risk based on text also had an area under the curve of only 0.632, but the ensemble model built by integrating text data and sociodemographic information was able to confirm the diagnostic usefulness with an area under the curve of 0.800. Conclusion: This study established an algorithm for diagnosing depression and suicide risk by extracting voice and speech features and text data from the subject's utterance section in a structured interview. Both data showed excellent performance in diagnosing depression, but insufficient for diagnosing suicide risk. In the case of text data, although there are limitations as data obtained through structured interviews, when it was integrated with sociodemographic information using statistical techniques, it showed better performance than predicted by sociodemographic information alone, showing utility value. This study is the first study in South Korea to prove the objective diagnostic value of voice and text data, and it is a challenge to a new field of psychiatry as a digital objective diagnostic tool. In the future, additional research is needed on data from more diverse regions and environments in the field. Keywords : depression, suicide risk, voice, text analysis, machine learning, clinical decision support system Student Number : 2018-25300์„œ๋ก : ์ „ ์„ธ๊ณ„์ ์œผ๋กœ ์šฐ์šธ์ฆ๊ณผ ์ž์‚ด์€ ๋ฐœ๋ณ‘๋ฅ ์ด ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๊ทธ๋กœ ์ธํ•œ ์‚ฌํšŒ๊ฒฝ์ œ์  ์†์‹ค์ด ๋ง‰๋Œ€ํ•˜๋‹ค. ํ•˜์ง€๋งŒ ์ด๋ฅผ ์ง„๋‹จํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ํ™˜์ž์˜ ์ฃผ๊ด€์ ์ธ ๋Œ€๋‹ต์„ ๋ฐ”ํƒ•์œผ๋กœ ์ง„๋‹จํ•˜๋Š” ๋ฐฉ๋ฒ• ๋ฟ์ด๋‹ค. ๊ทธ๋กœ ์ธํ•ด ํ™˜์ž๊ฐ€ ์ฆ์ƒ์„ ์ถ•์†Œํ•˜์—ฌ ๋ณด๊ณ ํ•˜๊ฑฐ๋‚˜ ์‹ฌ์ธต์ ์ธ ๋ฉด๋‹ด์„ ์ง„ํ–‰ํ•˜๊ธฐ ์–ด๋ ค์šด ์ƒํ™ฉ์—์„œ๋Š” ์ •ํ™•ํ•œ ์ง„๋‹จ์ด ์–ด๋ ต๊ณ , ํšจ์œจ์ ์ธ ๊ฐœ์ž…์„ ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋”ฐ๋ผ์„œ ์šฐ์šธ์ฆ๊ณผ ์ž์‚ด์„ ์ง„๋‹จํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ๊ด€์ ์ธ ๋งˆ์ปค๋“ค์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ ์ค‘ ์ธํ„ฐ๋ทฐ ์‹œ์˜ ๋ชฉ์†Œ๋ฆฌ์™€, ์‚ฌ์šฉํ•˜๋Š” ๋‹จ์–ด ๋“ฑ์€ ์ž„์ƒ์˜๊ฐ€ ์ถ•์ ๋œ ์ž„์ƒ๊ฒฝํ—˜์„ ๋ฐ”ํƒ•์œผ๋กœ ๋‚ด๋ฆฌ๋Š” ์ž„์ƒ์  ํŒ๋‹จ์— ๋งŽ์ด ํ™œ์šฉ๋˜์—ˆ๋˜ ์ง€ํ‘œ๋“ค์ด๋‹ค. ์ตœ๊ทผ ๋ชฉ์†Œ๋ฆฌ์˜ ๋‹ค์–‘ํ•œ ์ง€ํ‘œ๋ฅผ ์ถ”์ถœํ•˜๊ณ  ๋ฐœํ™” ๋‹จ์–ด๋ฅผ ์ถ”์ถœํ•˜๋Š” ๊ธฐ์ˆ ๋“ค์ด ๊ฐœ๋ฐœ๋จ์— ๋”ฐ๋ผ์„œ ์šฐ์šธ์ฆ๊ณผ ์ž์‚ด ์œ„ํ—˜์— ๋”ฐ๋ฅธ ๋ชฉ์†Œ๋ฆฌ์˜ ์ฐจ์ด์™€ ๋ฐœํ™” ๋‹จ์–ด๋“ค์˜ ์ฐจ์ด๋“ค์ด ๋ฐํ˜€์ง€๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ ์ธ๊ณต ์ง€๋Šฅ์ด ์˜๋ฃŒ๊ณ„์— ์ ‘๋ชฉ๋จ์— ๋”ฐ๋ผ์„œ ์ด๋Ÿฌํ•œ ๋ฏธ์„ธํ•œ ์ฐจ์ด๋“ค์„ ๊ฐ์ง€ํ•˜๊ณ  ์ž„์ƒ์  ์˜์‚ฌ ๊ฒฐ์ •์— ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ตญ๋‚ด์™ธ์˜ ์„ ํ–‰ ์—ฐ๊ตฌ๋“ค์—์„œ๋Š” ์•„์ง ํ”ผํ—˜์ž์˜ ์ˆ˜๊ฐ€ ์ถฉ๋ถ„ํ•˜์ง€ ์•Š๊ณ , ์•ฝ๋ฌผ ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ์ž„์ƒ์  ์ƒํ™ฉ๋“ค์— ๋Œ€ํ•œ ๊ณ ๋ ค๊ฐ€ ๋ถ€์กฑํ•˜๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ธฐ์กด์˜ ํ•œ๊ณ„๋“ค์„ ๊ทน๋ณตํ•˜์—ฌ, ์ธํ„ฐ๋ทฐ ์ค‘์˜ ํ”ผํ—˜์ž ๋ชฉ์†Œ๋ฆฌ์™€ ์‚ฌ์šฉ๋œ ๋‹จ์–ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์šฐ์šธ์ฆ๊ณผ ์ž์‚ด ์œ„ํ—˜๊ตฐ์„ ํ‰๊ฐ€ํ•˜๋Š” ์ธ๊ณต์ง€๋Šฅ ๊ธฐ๋ฐ˜ ์ž„์ƒ์˜์‚ฌ๊ฒฐ์ •์ง€์›์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•ด๋ณด๊ณ ์ž ํ•œ๋‹ค. ๋ฐฉ๋ฒ•: ์šฐ์šธํ•œ ์ฆ์ƒ์„ ํ˜ธ์†Œํ•˜๋Š” ํ™˜์ž๊ตฐ๊ณผ ์ •์ƒ๋Œ€์กฐ๊ตฐ์„ ๊ฐ๊ฐ ๋ชจ์ง‘ํ•˜์˜€๊ณ , ๋ชจ๋“  ํ”ผํ—˜์ž์—๊ฒŒ ๊ฐ„์ด ๊ตญ์ œ ์‹ ๊ฒฝ ์ •์‹  ์ธํ„ฐ๋ทฐ(Mini International Neuropsychiatric Interview)๋ฅผ ์‹œํ–‰ํ•˜์—ฌ ํ•ด๋‹น ์ธํ„ฐ๋ทฐ๋ฅผ ๋…น์Œํ•˜์˜€๋‹ค. ๋…น์Œ๋œ ์ธํ„ฐ๋ทฐ ํŒŒ์ผ ์ค‘ ํ”ผํ—˜์ž๊ฐ€ ๋ฐœํ™”ํ•œ ๊ตฌ๊ฐ„๋งŒ์„ ์ถ”์ถœํ•œ ๋’ค, ๊ฐ ๊ตฌ๊ฐ„์—์„œ ๋ชฉ์†Œ๋ฆฌ์˜ ๋‹ค์–‘ํ•œ ์ง€ํ‘œ๋“ค๊ณผ ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ๋ฅผ ์ถ”์ถœํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ I์—์„œ๋Š” 2์ฐจ ๋…„๋„๊นŒ์ง€์˜ ์ดˆ๊ธฐ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ •์ƒ๊ตฐ๊ณผ ๊ฒฝ๋„ ์šฐ์šธ์ฆ๊ตฐ, ์ฃผ์š” ์šฐ์šธ์ฆ๊ตฐ์„ ๋น„๊ตํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์—ฐ๊ตฌ II์™€ III์„ ํ†ตํ•ด ์ตœ์ข…์ ์œผ๋กœ ๋ชจ์ง‘๋œ ์ดˆ๊ธฐ ํ‰๊ฐ€ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์šฐ์šธ์ฆ๊ณผ ์ž์‚ด ์œ„ํ—˜์„ ํ‰๊ฐ€ํ•˜๋Š” ์ธ๊ณต์ง€๋Šฅ ๊ธฐ๋ฐ˜ ์ž„์ƒ์˜์‚ฌ๊ฒฐ์ •์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•ด๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ์ž์‚ด ์œ„ํ—˜์€ ๋นˆ๋„๊ฐ€ ์ž‘๊ธฐ ๋•Œ๋ฌธ์—, ์ž์‚ด ์œ„ํ—˜์„ ์ •ํ™•ํ•˜๊ฒŒ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์—ฐ๊ตฌ II๋ฅผ ํ†ตํ•ด ์Œ์„ฑ๊ณผ ํ…์ŠคํŠธ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ •์ƒ๊ณผ ์šฐ์šธ์ฆ์„ ๊ตฌ๋ณ„ํ•  ์ˆ˜ ์žˆ๋Š” ํ‰๊ฐ€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ๊ทธ ํ›„ 2๋‹จ๊ณ„๋กœ์„œ ์—ฐ๊ตฌ III์„ ํ†ตํ•ด ์šฐ์šธ์ฆ ๊ตฐ ๋‚ด์—์„œ ์ž์‚ด ์ €์œ„ํ—˜๊ตฐ๊ณผ ์ž์‚ด ๊ณ ์œ„ํ—˜๊ตฐ์„ ๊ตฌ๋ถ„ํ•˜์—ฌ ์ž์‚ด ์œ„ํ—˜์„ ํ‰๊ฐ€ํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ตฌ์ถ•ํ•˜์—ฌ ๊ทธ ์„ฑ๋Šฅ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ: 2์ฐจ ๋…„๋„๊นŒ์ง€ ๋ชจ์ง‘๋œ ์ •์ƒ๊ตฐ 33๋ช…๊ณผ ๊ฒฝ๋„ ์šฐ์šธ์ฆ๊ตฐ 26๋ช…, ์ฃผ์š” ์šฐ์šธ์ฆ๊ตฐ 34๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ๋ถ„์„ํ•œ ์—ฐ๊ตฌ I์˜ ๊ฒฐ๊ณผ, ์„ธ ๊ตฐ์„ ๊ตฌ๋ณ„ํ•  ์ˆ˜ ์žˆ๋Š” ์Œ์„ฑ ์ง€ํ‘œ 7๊ฐœ๋ฅผ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋˜ํ•œ ์Œ์„ฑ ์ง€ํ‘œ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์„ธ ๊ตฐ์„ ํ‰๊ฐ€ํ•˜๋Š” ์ง„๋‹จ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๋‹ค์–‘ํ•œ ๋ชจ๋ธ์„ ํ†ตํ•ด ๊ตฌ์ถ•ํ•˜์˜€๊ณ , ๋‹ค์ธต ํผ์…‰ํŠธ๋ก ์˜ ์„ฑ๋Šฅ์ด ๊ฐ€์žฅ ์šฐ์ˆ˜ํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. 3์ฐจ ๋…„๋„๊นŒ์ง€ ์ตœ์ข…์ ์œผ๋กœ ๋ชจ์ง‘๋œ ํ™˜์ž๋Š” ์ด 85๋ช…์ด์—ˆ๊ณ , ๊ทธ ์ค‘ ์ž๊ฐ€๋ณด๊ณ ์„ค๋ฌธ์ง€๋ฅผ ๋ˆ„๋ฝํ•œ 2๋ช…์€ ๋ถ„์„์—์„œ ์ œ์™ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ •์ƒ๋Œ€์กฐ๊ตฐ์€ ํ•™๋‚ด ๊ฒŒ์‹œํŒ๊ณผ ์˜จ๋ผ์ธ ๊ด‘๊ณ ๋ฅผ ํ†ตํ•ด ์ด 105๋ช…์ด ๋ชจ์ง‘๋˜์—ˆ์œผ๋ฉฐ ์ •์‹ ๊ฑด๊ฐ•์˜ํ•™๊ณผ ๋ณ‘๋ ฅ์ด ์žˆ๋Š” 22๋ช…์€ ๋ถ„์„์—์„œ ์ œ์™ธ๋˜์—ˆ๋‹ค. ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ง„ํ–‰๋œ ์—ฐ๊ตฌ II์—์„œ๋Š” ์Œ์„ฑ ์ง€ํ‘œ์™€ ํ…์ŠคํŠธ๋ฐ์ดํ„ฐ๋ฅผ ๋ถ„์„ํ•˜์—ฌ ์ •์ƒ๊ตฐ๊ณผ ์šฐ์šธ์ฆ๊ตฐ์„ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๋Š” ์ธ๊ณต์ง€๋Šฅ ๊ธฐ๋ฐ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ตฌ์ถ•ํ•˜์˜€๊ณ , ์Œ์„ฑ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ณก์„ ํ•˜๋ฉด์ ์€ 0.806, ํ…์ŠคํŠธ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ณก์„ ํ•˜๋ฉด์ ์€ 0.905๋กœ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด์–ด ์—ฐ๊ตฌ III์˜ ์šฐ์šธ์ฆ๊ตฐ 83๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ์ž์‚ด ๊ณ ์œ„ํ—˜๊ตฐ์„ ๊ตฌ๋ถ„ํ•˜๋Š” ์ธ๊ณต์ง€๋Šฅ ์ž„์ƒ์˜์‚ฌ๊ฒฐ์ •์ง€์›์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ์ž์‚ด ๊ณ ์œ„ํ—˜๊ตฐ์„ ๊ตฌ๋ถ„ํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ๊ฐ๊ฐ ๋ฒก ์ž์‚ด์‚ฌ๊ณ  ์ฒ™๋„ ๊ธฐ์ค€์˜ ๋ฐฉ๋ฒ•๊ณผ, ๊ฐ„์ด ๊ตญ์ œ ์‹ ๊ฒฝ์ •์‹  ์ธํ„ฐ๋ทฐ๋ฅผ ํ†ตํ•ด ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๊ตฌ๋ถ„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ๋ชจ๋‘ ์ ์šฉํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์Œ์„ฑ์ง€ํ‘œ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ž์‚ด ๊ณ ์œ„ํ—˜๊ตฐ์„ ํ‰๊ฐ€ํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ฒฝ์šฐ ๋ฏผ๊ฐ๋„ 0.535๊ฐ€ ์ตœ๋Œ€๊ฐ’์ด์—ˆ์œผ๋ฉฐ, ๊ฐ€์žฅ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์€ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๊ณต์‹์„ ์ด์šฉํ•œ ๋ชจ๋ธ์—์„œ ๋ณด์ธ ํ‰๊ท  ์ •ํ™•๋„ 0.495์˜€๋‹ค. ๋˜ํ•œ ํ…์ŠคํŠธ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ž์‚ด ์œ„ํ—˜์„ ํ‰๊ฐ€ํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์—ญ์‹œ ๊ณก์„ ํ•˜๋ฉด์  0.632์— ๋ถˆ๊ณผํ•˜์—ฌ, ๋ชฉ์†Œ๋ฆฌ์™€ ํ…์ŠคํŠธ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ž์‚ด ๊ณ ์œ„ํ—˜๊ตฐ ์ง„๋‹จ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ž„์ƒ์ ์œผ๋กœ ํ™œ์šฉํ•˜๊ธฐ๋Š” ์–ด๋ ค์šด ์ˆ˜์ค€์ด์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ์™€ ์‚ฌํšŒ์ธ๊ตฌํ•™์  ์ •๋ณด๋ฅผ ํ†ตํ•ฉํ•˜์—ฌ ๊ตฌ์ถ•ํ•œ ์•™์ƒ๋ธ” ๋ชจ๋ธ์˜ ๊ณก์„ ํ•˜๋ฉด์ ์€ 0.800์œผ๋กœ ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ฒฐ๋ก : ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตฌ์กฐํ™”๋œ ๋ฉด๋‹ด์—์„œ ํ”ผํ—˜์ž์˜ ๋ฐœํ™” ๊ตฌ๊ฐ„์„ ํ†ตํ•ด ์Œ์„ฑ๊ณผ ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ๋ฅผ ์ถ”์ถœํ•˜์—ฌ, ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์šฐ์šธ์ฆ๊ณผ ์ž์‚ด ์œ„ํ—˜์„ฑ์„ ํ‰๊ฐ€ํ•˜๋Š” ์ž„์ƒ์˜์‚ฌ๊ฒฐ์ •์ง€์› ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ตญ๋‚ด์—์„œ๋Š” ์ตœ์ดˆ๋กœ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ์šฐ์šธ์ฆ์„ ์ง„๋‹จํ•จ์— ์žˆ์–ด์„œ๋Š” ๋‘ ๋ฐ์ดํ„ฐ ๋ชจ๋‘ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์˜€์œผ๋‚˜, ์ž์‚ด ์œ„ํ—˜์„ ์ง„๋‹จํ•˜๊ธฐ์—๋Š” ๋ถ€์กฑํ•˜์˜€๋‹ค. ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ์˜ ๊ฒฝ์šฐ ๊ตฌ์กฐํ™”๋œ ์ธํ„ฐ๋ทฐ๋ฅผ ํ†ตํ•ด ์–ป์–ด์ง„ ํ…์ŠคํŠธ๋ผ๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์šฐ์šธ์ฆ์˜ ์ง„๋‹จํ•จ์— ์žˆ์–ด ์ž„์ƒ์˜์‚ฌ๊ฒฐ์ •์ง€์›์‹œ์Šคํ…œ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋”์šฑ์ด ์ž์‚ด ์œ„ํ—˜ ์ง„๋‹จ์— ์žˆ์–ด์„œ๋„ ์‚ฌํšŒ์ธ๊ตฌํ•™์  ์ •๋ณด์™€ ํ†ตํ•ฉ๋œ ์•™์ƒ๋ธ”๋ชจ๋ธ์˜ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ํ™•์ธํ•จ์œผ๋กœ์จ ํ–ฅํ›„ ์ž„์ƒ์  ํ™œ์šฉ๊ฐ€์น˜๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ชฉ์†Œ๋ฆฌ, ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ๊ฐ€ ๊ฐ€์ง€๋Š” ๊ฐ๊ด€์  ์ง„๋‹จ ๊ฐ€์น˜๋ฅผ ๊ตญ๋‚ด ์ตœ์ดˆ๋กœ ์ž…์ฆํ•œ ์—ฐ๊ตฌ๋กœ์„œ, ๋””์ง€ํ„ธ ์ง„๋‹จ ๋„๊ตฌ๋ผ๋Š” ์ •์‹ ๊ฑด๊ฐ•์˜ํ•™๊ณผ์ ์œผ๋กœ๋Š” ์ƒˆ๋กœ์šด ๋ถ„์•ผ๋กœ์˜ ๋„์ „์ด๋‹ค. ํ–ฅํ›„ ํ•ด๋‹น ๋ถ„์•ผ์˜ ๋” ๋‹ค์–‘ํ•œ ์ง€์—ญ, ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ์—์„œ์˜ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด์„œ ์ถ”๊ฐ€์ ์ธ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. * ๋ณธ ๋‚ด์šฉ์˜ ์ผ๋ถ€๋Š” Shin, D., Cho, W. I., Park, C., Rhee, S. J., Kim, M. J., Lee, H., ... & Ahn, Y. M. (2021). Detection of Minor and Major Depression through Voice as a Biomarker Using Machine Learning. Journal of clinical medicine, 10(14), 3046. ์— ์ถœํŒ ์™„๋ฃŒ๋œ ๋‚ด์šฉ์ด๋ฉฐ, ๊ทธ ์™ธ ๋‚ด์šฉ๋“ค์€ ํ˜„์žฌ ์ถœํŒ ์ค€๋น„ ์ค‘์ž„. ์ฃผ์š”์–ด : ์šฐ์šธ์ฆ, ์ž์‚ด ์œ„ํ—˜, ๋ชฉ์†Œ๋ฆฌ, ํ…์ŠคํŠธ, ๊ธฐ๊ณ„ํ•™์Šต, ์ž„์ƒ์˜์‚ฌ๊ฒฐ์ •์ง€์›์‹œ์Šคํ…œ ํ•™ ๋ฒˆ : 2018-25300์ดˆ๋ก i ํ‘œ ๋ชฉ์ฐจ vi ๊ทธ๋ฆผ ๋ชฉ์ฐจ ix ์•ฝ์–ด ๋ชฉ๋ก x ์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 2 ์žฅ ๋ชฉ์†Œ๋ฆฌ๋ฅผ ํ†ตํ•ด ๊ฒฝ๋„ ์šฐ์šธ์ฆ๊ณผ ์ฃผ์š” ์šฐ์šธ์ฆ์„ ๊ตฌ๋ณ„ํ•˜๋Š” ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ฐœ๋ฐœ (์—ฐ๊ตฌ I) 10 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ• 11 ์ œ 2 ์ ˆ ๊ฒฐ๊ณผ 22 ์ œ 3 ์ ˆ ๊ณ ์ฐฐ 26 ์ œ 3 ์žฅ ๋ชฉ์†Œ๋ฆฌ์™€ ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์šฐ์šธ์ฆ ์ง„๋‹จ ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ฐœ๋ฐœ (์—ฐ๊ตฌ II) 31 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ• 33 ์ œ 2 ์ ˆ ๊ฒฐ๊ณผ 38 ์ œ 3 ์ ˆ ๊ณ ์ฐฐ 43 ์ œ 4 ์žฅ ๋ชฉ์†Œ๋ฆฌ์™€ ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ๋ฅผ ํ†ตํ•ด ์ž์‚ด๊ณ ์œ„ํ—˜๊ตฐ์„ ์ง„๋‹จํ•˜๋Š” ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฐœ๋ฐœ (์—ฐ๊ตฌ III) 49 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ• 51 ์ œ 2 ์ ˆ ๊ฒฐ๊ณผ 54 ์ œ 3 ์ ˆ ๊ณ ์ฐฐ 59 ์ œ 5 ์žฅ ์ข…ํ•ฉ ๊ณ ์ฐฐ 65 ์ฐธ๊ณ ๋ฌธํ—Œ 128 Abstract 152 ํ‘œ ๋ชฉ์ฐจ [ํ‘œ 1-1] Comparison of demographics according to depressive episodes 78 [ํ‘œ 1-2] Clinical characteristics by depressive episode 80 [ํ‘œ 1-3] Difference of voice features by depressive episode 81 [ํ‘œ 1-4 JonckheereTerpstra test result of voice features by depressive episode 83 [ํ‘œ 1-5] Machine learning model performance through voice features 85 [ํ‘œ 1-6] Comparison of demographics between healthy control and major depressive disorder and bipolar disorder 86 [ํ‘œ 1-7] Comparison of clinical characteristics between healthy control and major depressive disorder and bipolar disorder 87 [ํ‘œ 1-8] Voice and speech features between healthy control and major depressive disorder and bipolar disorder 88 [ํ‘œ 2-1] Descriptive statistics of voice and speech features 90 [ํ‘œ 2-2] The results of applying the diagnostic algorithm from Study I to the newly recruited test set from Study II 91 [ํ‘œ 2-3] Demographics comparison between healthy control group and current depression group 92 [ํ‘œ 2-4] Clinical characteristics between HC and CD 93 [ํ‘œ 2-5] Voice and speech features comparison between healthy control and current depression 94 [ํ‘œ 2-6] Classification results by voice and speech features between HC and CD 96 [ํ‘œ 2-7] Classification results by text between HC and CD 97 [ํ‘œ 2-8] Correlation analysis between voice and speech features and clinical characteristics in healthy control group 98 [ํ‘œ 2-9] Correlation analysis between voice and speech features and clinical characteristics in current depression group 100 [ํ‘œ 2-10] Mediation analysis between voice and speech features and clinical characteristics in healthy control group 102 [ํ‘œ 2-11] Mediation analysis between voice and speech features and clinical characteristics in current depression group 103 [ํ‘œ 3-1] Demographics between depression with low suicidal risk and depression with high suicidal risk (suicidal risk : BSS) 104 [ํ‘œ 3-2] Clinical characteristics between depression with low suicidal risk and depression with high suicidal risk (suicidal risk : BSS) 105 [ํ‘œ 3-3] Voice and Speech features between DLSR and DHSR (suicidal risk : BSS) 106 [ํ‘œ 3-4] Classification results by voice and speech features between DLSR and DHSR (suicidal risk; BSS) 108 [ํ‘œ 3-5] Classification results by voice and speech features between DLSR and DHSR (suicidal risk : BSS) 109 [ํ‘œ 3-6] Demographics between depression with low suicidal risk and depression with high suicidal risk (suicidal risk : MINI) 110 [ํ‘œ 3-7] Clinical characteristics between low suicidal risk and depression with high suicidal risk (suicidal risk : MINI) 111 [ํ‘œ 3-8] Voice and Speech features between DLSR and DHSR (suicidal risk : MINI) 112 [ํ‘œ 3-9] Classification results by voice and speech features between DLSR and DHSR (suicidal risk; MINI) 114 [ํ‘œ 3-10] Classification results by voice and speech features between DLSR and DHSR (suicidal risk : MINI) 115 ๊ทธ๋ฆผ ๋ชฉ์ฐจ [๊ทธ๋ฆผ 1-1] Difference of voice features by depressive episode 116 [๊ทธ๋ฆผ 1-2] AUC curve predicting minor and major episodes using MLP 117 [๊ทธ๋ฆผ 2-1] ROC curve of classification HC and CD by voice and speech features 118 [๊ทธ๋ฆผ 2-2] Feature importance for classifying HC and CD 119 [๊ทธ๋ฆผ 2-3] Text mining of important features in HC and CD 120 [๊ทธ๋ฆผ 2-4] Classification results between HC and CD by text 121 [๊ทธ๋ฆผ 2-5] Mediation analysis between voice and speech feature and clinical characteristics in healthy control group 122 [๊ทธ๋ฆผ 2-6] Mediation analysis between voice and speech feature and clinical characteristics in current depression group 123 [๊ทธ๋ฆผ 3-1] ROC curve of classification DLSR and DHSR by voice and speech features (suicidal risk : BSS) 124 [๊ทธ๋ฆผ 3-2] Classification results of DLSR and DHSR by text and clinical characteristics (suicidal risk : BSS) 125 [๊ทธ๋ฆผ 3-3] ROC curve of classification DLSR and DHSR by voice and speech features (suicidal risk : MINI) 126 [๊ทธ๋ฆผ 3-4] Classification results of DLSR and DHSR by text and clinical characteristics (suicidal risk : MINI) 127๋ฐ•

    Jaw Rotation in Dysarthria Measured With a Single Electromagnetic Articulography Sensor

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    Purpose This study evaluated a novel method for characterizing jaw rotation using orientation data from a single electromagnetic articulography sensor. This method was optimized for clinical application, and a preliminary examination of clinical feasibility and value was undertaken. Method The computational adequacy of the single-sensor orientation method was evaluated through comparisons of jaw-rotation histories calculated from dual-sensor positional data for 16 typical talkers. The clinical feasibility and potential value of single-sensor jaw rotation were assessed through comparisons of 7 talkers with dysarthria and 19 typical talkers in connected speech. Results The single-sensor orientation method allowed faster and safer participant preparation, required lower data-acquisition costs, and generated less high-frequency artifact than the dual-sensor positional approach. All talkers with dysarthria, regardless of severity, demonstrated jaw-rotation histories with more numerous changes in movement direction and reduced smoothness compared with typical talkers. Conclusions Results suggest that the single-sensor orientation method for calculating jaw rotation during speech is clinically feasible. Given the preliminary nature of this study and the small participant pool, the clinical value of such measures remains an open question. Further work must address the potential confound of reduced speaking rate on movement smoothness

    Is voice therapy an effective treatment for dysphonia? A randomised controlled trial

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    OBJECTIVES: To assess the overall efficacy of voice therapy for dysphonia. DESIGN: Single blind randomised controlled trial. SETTING: Outpatient clinic in a teaching hospital. Participants: 204 outpatients aged 17-87 with a primary symptom of persistent hoarseness for at least two months. INTERVENTIONS: After baseline assessments, patients were randomised to six weeks of either voice therapy or no treatment. Assessments were repeated at six weeks on the 145 (71%) patients who continued to this stage and at 12-14 weeks on the 133 (65%) patients who completed the study. The assessments at the three time points for the 70 patients who completed treatment and the 63 patients in the group given no treatment were compared. MAIN OUTCOME MEASURES: Ratings of laryngeal features, Buffalo voice profile, amplitude and pitch perturbation, voice profile questionnaire, hospital anxiety and depression scale, clinical interview schedule, SF-36. RESULTS: Voice therapy improved voice quality as assessed by rating by patients (P=0.001) and rating by observer (P<0.001). The treatment effects for these two outcomes were 4.1 (95% confidence interval 1.7 to 6.6) points and 0.82 (0.50 to 1.13) points. Amplitude perturbation showed improvement at six weeks (P=0.005) but not on completion of the study. Patients with dysphonia had appreciable psychological distress and lower quality of life than controls, but voice therapy had no significant impact on either of these variables. CONCLUSION: Voice therapy is effective in improving voice quality as assessed by self rated and observer rated methods
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