Hybrid Depression Classification and Estimation from Audio Video and Text Information

Yang, Le; Sahli, Hichem; Xia, Xiaohan; Pei, Ercheng; Oveneke, Meshia Cédric; Jiang, Dongmei

doi:10.1145/3133944.3133950

Cited by 71 publications

(47 citation statements)

References 15 publications

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“…Their strengths lie in their ability to represent information through non-linear transforms, at varying spatial and temporal resolution, and from multiple modalities [17,18]. While work in the domain of detecting depression has looked at fusing features from multiple modalities together [9,13,14,19], and utilizing neural networks to model single sequences [10,15], there remains to explore the sequence modeling of depression that utilizes deep learning approaches.…”

Section: Introductionmentioning

confidence: 99%

Detecting Depression with Audio/Text Sequence Modeling of Interviews

Alhanai¹,

2018

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Medical professionals diagnose depression by interpreting the responses of individuals to a variety of questions, probing lifestyle changes and ongoing thoughts. Like professionals, an effective automated agent must understand that responses to queries have varying prognostic value. In this study we demonstrate an automated depression-detection algorithm that models interviews between an individual and agent and learns from sequences of questions and answers without the need to perform explicit topic modeling of the content. We utilized data of 142 individuals undergoing depression screening, and modeled the interactions with audio and text features in a Long-Short Term Memory (LSTM) neural network model to detect depression. Our results were comparable to methods that explicitly modeled the topics of the questions and answers which suggests that depression can be detected through sequential modeling of an interaction, with minimal information on the structure of the interview.

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Section: Introductionmentioning

confidence: 99%

Detecting Depression with Audio/Text Sequence Modeling of Interviews

Alhanai¹,

2018

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“…In [38], both conversation-level (number of sentences, number of words used, etc.) and content-level (feeling good/bad, extrovert/introvert personality, etc.)…”

Section: Fusion Of Text and Audio Featuresmentioning

confidence: 99%

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

Demiroğlu

Beşirli

Özkanca

et al. 2020

J AUDIO SPEECH MUSIC PROC.

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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.

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“…In the challenge, two papers, both lead by Le Yang of Northwestern Polytechnical University, explored the suitability of CNNs for the task. The first [131] system fed ComParE-2013 features into a CCN trained to predict PHQ8 score. After training the CNN, the weights were frozen and the last layer removed.…”

Section: Depression (2016 and 2017)mentioning

confidence: 99%

Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning

Cummins

Baird

Schuller

2018

Methods

135

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Due to the complex and intricate nature associated with their production, the acoustic-prosodic properties of a speech signal are modulated with a range of health related effects. There is an active and growing area of machine learning research in this speech and health domain, focusing on developing paradigms to objectively extract and measure such effects. Concurrently, deep learning is transforming intelligent signal analysis, such that machines are now reaching near human capabilities in a range of recognition and analysis tasks. Herein, we review current state-of-the-art approaches with speech-based health detection, placing a particular focus on the impact of deep learning within this domain. Based on this overview, it is evident while that deep learning based solutions be become more present in the literature, it has not had the same overall dominating effect seen in other related fields. In this regard, we suggest some possible research directions aimed at fully leveraging the advantages that deep learning can offer speech-based health detection.

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Hybrid Depression Classification and Estimation from Audio Video and Text Information

Cited by 71 publications

References 15 publications

Detecting Depression with Audio/Text Sequence Modeling of Interviews

Detecting Depression with Audio/Text Sequence Modeling of Interviews

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

Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning

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