Project

H2020-RISE MENHIR (grant no. 823907) - Mental health monitoring through interactive conversations

Goal: Mental health is an essential component of health; however depression and anxiety are common disorders across the European Union. Many people may be described as “living with” a mental illness, and managing their own symptoms. However, they are often unsure of the thresholds for treatment, how to control their mental health, what are the best coping strategies or which resources are available to them. Conversational systems may facilitate watchful waiting and symptom monitoring, by initiating contact and symptom checking at various times of the day and night. The MENHIR project aims to research and develop conversational technologies to promote mental health and assist people with mental ill health (depression and anxiety) to manage their conditions.

http://menhir-project.eu

Date: 1 February 2019 - 1 February 2023

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Zoraida Callejas
added 9 research items
Mental health and mental wellbeing have become an important factor to many citizens navigating their way through their environment and in the work place. New technology solutions such as chatbots are potential channels for supporting and coaching users to maintain a good state of mental wellbeing. Chatbots have the added value of providing social conversations and coaching 24/7 outside from conventional mental health services. However, little is known about the acceptability and user led requirements of this technology. This paper uses a living lab approach to elicit requirements, opinions and attitudes towards the use of chatbots for supporting mental health. The data collected was acquired from people living with anxiety or mild depression in a workshop setting. The audio of the workshop was recorded and a thematic analysis was carried out. The results are the co-created functional requirements and a number of use case scenarios that can be of interest to guide future development of chatbots in the mental health domain.
In this paper, we present a proposal for emotion recognition using audio speech signal features consisting of two functionally independent systems. First, a voice activity detection module (VAD) acts as a filter prior to the emotion classification task. It extracts features from the input audio and uses a SVM classifier to predict the presence of voice activity. Secondly, the speech emotion classifier (EMO) transforms the power spectrum of the signal to a Mel scale and obtains a vector of its characteristics using a convolutional neural network. Emotion labels are assigned using this vector and a KNN classifier. The RAVDESS dataset has been used for training the models obtaining a maximum accuracy of 93.57% classifying 8 emotions.
Zoraida Callejas
added 3 research items
Check full text here: http://hdl.handle.net/10481/58919 https://www.isca-speech.org/archive/Interspeech_2019/abstracts/2230.html
Devices with oral interfaces are enabling new interesting interaction scenarios and ways of interaction in ambient intelligence settings. The use of several of such devices in the same environment opens up the possibility to compare the inputs gathered from each one of them and perform a more accurate recognition and processing of user speech. However, the combination of multiple devices presents coordination challenges, as the processing of one voice signal by different speech processing units may result in conflicting outputs and it is necessary to decide which is the most reliable source. This paper presents an approach to rank several sources of spoken input in multi-device environments in order to give preference to the input with the highest estimated quality. The voice signals received by the multiple devices are assessed in terms of their calculated acoustic quality and the reliability of the speech recognition hypotheses produced. After this assessment, each input is assigned a unique score that allows the audio sources to be ranked so as to pick the best to be processed by the system. In order to validate this approach, we have performed an evaluation using a corpus of 4608 audios recorded in a two-room intelligent environment with 24 microphones. The experimental results show that our ranking approach makes it possible to successfully orchestrate an increasing number of acoustic inputs, obtaining better recognition rates than considering a single input, both in clear and noisy settings.
Zoraida Callejas
added 4 project references
Zoraida Callejas
added an update
MENHIR web page is online:
 
Zoraida Callejas
added a project goal
Mental health is an essential component of health; however depression and anxiety are common disorders across the European Union. Many people may be described as “living with” a mental illness, and managing their own symptoms. However, they are often unsure of the thresholds for treatment, how to control their mental health, what are the best coping strategies or which resources are available to them. Conversational systems may facilitate watchful waiting and symptom monitoring, by initiating contact and symptom checking at various times of the day and night. The MENHIR project aims to research and develop conversational technologies to promote mental health and assist people with mental ill health (depression and anxiety) to manage their conditions.