722 research outputs found
AVEC 2019 workshop and challenge: state-of-mind, detecting depression with AI, and cross-cultural affect recognition
The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind,
Detecting Depression with AI, and Cross-cultural Affect Recognition" is the
ninth competition event aimed at the comparison of multimedia processing and
machine learning methods for automatic audiovisual health and emotion analysis,
with all participants competing strictly under the same conditions. The goal of
the Challenge is to provide a common benchmark test set for multimodal
information processing and to bring together the health and emotion recognition
communities, as well as the audiovisual processing communities, to compare the
relative merits of various approaches to health and emotion recognition from
real-life data. This paper presents the major novelties introduced this year,
the challenge guidelines, the data used, and the performance of the baseline
systems on the three proposed tasks: state-of-mind recognition, depression
assessment with AI, and cross-cultural affect sensing, respectively
Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing
It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares
Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks
This paper proposes a speech-based method for automatic depression
classification. The system is based on ensemble learning for Convolutional
Neural Networks (CNNs) and is evaluated using the data and the experimental
protocol provided in the Depression Classification Sub-Challenge (DCC) at the
2016 Audio-Visual Emotion Challenge (AVEC-2016). In the pre-processing phase,
speech files are represented as a sequence of log-spectrograms and randomly
sampled to balance positive and negative samples. For the classification task
itself, first, a more suitable architecture for this task, based on
One-Dimensional Convolutional Neural Networks, is built. Secondly, several of
these CNN-based models are trained with different initializations and then the
corresponding individual predictions are fused by using an Ensemble Averaging
algorithm and combined per speaker to get an appropriate final decision. The
proposed ensemble system achieves satisfactory results on the DCC at the
AVEC-2016 in comparison with a reference system based on Support Vector
Machines and hand-crafted features, with a CNN+LSTM-based system called
DepAudionet, and with the case of a single CNN-based classifier
Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks
This paper proposes a speech-based method for automatic depression classification.
The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is
evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio–Visual Emotion Challenge (AVEC-2016). In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples. For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built.
Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision. The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier.This research was partly funded by Spanish Government grant TEC2017-84395-P
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