Depression-level assessment from multi-lingual conversational speech data using acoustic and text features

Name: Depression-level assessment from multi-lingual conversational speech data using acoustic and text features
Author: Demiroğlu, Cenk, Besirli, A., Özkanca, Yasin Sedar, Celik, S.

İsim	Depression-level assessment from multi-lingual conversational speech data using acoustic and text features
Yazar	Demiroğlu, Cenk, Besirli, A., Özkanca, Yasin Sedar, Celik, S.
Basım Tarihi:	2020-11-17
Basım Yeri	- Springer Nature
Konu	Depression detection, Acoustic features, Feature selection
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1687-4722
Kayıt Numarası	e2d767e8-fb6d-422f-86cd-6256f0a9d516
Lokasyon	Electrical & Electronics Engineering
Tarih	2020-11-17
Örnek Metin	Depression is a widespread mental health problem around the world with a significant burden on economies. Its early diagnosis and treatment are critical to reduce the costs and even save lives. One key aspect to achieve that goal is to use technology and monitor depression remotely and relatively inexpensively using automated agents. There has been numerous efforts to automatically assess depression levels using audiovisual features as well as text-analysis of conversational speech transcriptions. However, difficulty in data collection and the limited amounts of data available for research present challenges that are hampering the success of the algorithms. One of the two novel contributions in this paper is to exploit databases from multiple languages for acoustic feature selection. Since a large number of features can be extracted from speech, given the small amounts of training data available, effective data selection is critical for success. Our proposed multi-lingual method was effective at selecting better features than the baseline algorithms, which significantly improved the depression assessment accuracy. The second contribution of the paper is to extract text-based features for depression assessment and use a novel algorithm to fuse the text- and speech-based classifiers which further boosted the performance.
DOI	10.1186/s13636-020-00182-4
Cilt	2020

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Depression-level assessment from multi-lingual conversational speech data using acoustic and text features

Yazar Demiroğlu, Cenk, Besirli, A., Özkanca, Yasin Sedar, Celik, S.

Basım Tarihi 2020-11-17

Basım Yeri - Springer Nature

Konu Depression detection, Acoustic features, Feature selection

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1687-4722

Kayıt Numarası e2d767e8-fb6d-422f-86cd-6256f0a9d516

Lokasyon Electrical & Electronics Engineering

Tarih 2020-11-17

Örnek Metin Depression is a widespread mental health problem around the world with a significant burden on economies. Its early diagnosis and treatment are critical to reduce the costs and even save lives. One key aspect to achieve that goal is to use technology and monitor depression remotely and relatively inexpensively using automated agents. There has been numerous efforts to automatically assess depression levels using audiovisual features as well as text-analysis of conversational speech transcriptions. However, difficulty in data collection and the limited amounts of data available for research present challenges that are hampering the success of the algorithms. One of the two novel contributions in this paper is to exploit databases from multiple languages for acoustic feature selection. Since a large number of features can be extracted from speech, given the small amounts of training data available, effective data selection is critical for success. Our proposed multi-lingual method was effective at selecting better features than the baseline algorithms, which significantly improved the depression assessment accuracy. The second contribution of the paper is to extract text-based features for depression assessment and use a novel algorithm to fuse the text- and speech-based classifiers which further boosted the performance.

DOI 10.1186/s13636-020-00182-4

Cilt 2020