A novel framework for distress detection through an automated speech processing system

Based on our ongoing work, this work in progress project aims to develop an automated system to detect distress in people to enable early referral for interventions to target anxiety and depression, to mitigate suicidal ideation and to improve adherence to treatment. The project will utilize either use existing voice data to assess people into various scales of distress, or will collect voice data as per existing standards of distress measurement, to develop basic computing algorithms required to detect various attributes associated with distress, detected through a person’s voice in a telephone call to a helpline. This will be then matched with the already available psychological assessment instruments such as the Distress Thermometer for these persons. In order to trigger interventions, organizational contexts are essential as interventions rely on the type of distress. Therefore, the model will be tested on various organizational settings such as the Police, Emergency and Health along with the Distress detection instruments normally used in a psychological assessment for accuracy and validation. The outcome of the project will culminate in a fully automated integrated system, and will save significant resources to organizations. The translation of the project will be realized in step-change improvements to quality of life within the gamut of public policy

Automatic Detection of Self-Adaptors for Psychological Distress

Psychological distress is a significant and growing issue in society. Automatic detection, assessment, and analysis of such distress is an active area of research. Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse. This is, in part, due to the lack of available datasets and difficulty in automatically extracting useful body features. Recent advances in pose estimation and deep learning have enabled new approaches to this modality and domain. We propose a novel method to automatically detect self-adaptors and fidgeting, a subset of self-adaptors that has been shown to be correlated with psychological distress. We also propose a multi-modal approach that combines different feature representations using Multi-modal Deep Denoising Auto-Encoders and Improved Fisher Vector encoding. We also demonstrate that our proposed model, combining audio-visual features with automatically detected fidgeting behavioral cues, can successfully predict distress levels in a dataset labeled with self-reported anxiety and depression levels. To enable this research we introduce a new dataset containing full body videos for short interviews and self-reported distress labels.King's College, Cmabridg

Recognition of Voice Commands by Multisource ASR and Noise Cancellation in a Smart Home Environment

International audienceIn this paper, we present a multisource ASR system to detect home automation orders in various everyday listening conditions in a realistic home. The system is based on a state of the art echo cancellation stage that feeds recently introduced ASR techniques. The evaluation was conducted on a realistic noisy data set acquired in a smart home where a microphone was placed near the noise source and several other microphones were placed in different rooms. This distant speech corpus was recorded with 23 speakers uttering colloquial or distress sentences as well as home automation orders. Techniques acting at the decoding stage and using a priori knowledge gave the best results in noisy condition compared to the baseline (recall= 93.2% vs 59.2%) reaching good enough performance for a real usage although improvement still need to be made when music is used as background noise

Recognition of Voice Commands by Multisource ASR and Noise Cancellation in a Smart Home Environment

International audienceIn this paper, we present a multisource ASR system to detect home automation orders in various everyday listening conditions in a realistic home. The system is based on a state of the art echo cancellation stage that feeds recently introduced ASR techniques. The evaluation was conducted on a realistic noisy data set acquired in a smart home where a microphone was placed near the noise source and several other microphones were placed in different rooms. This distant speech corpus was recorded with 23 speakers uttering colloquial or distress sentences as well as home automation orders. Techniques acting at the decoding stage and using a priori knowledge gave the best results in noisy condition compared to the baseline (recall= 93.2% vs 59.2%) reaching good enough performance for a real usage although improvement still need to be made when music is used as background noise

The CIRDO Corpus: Comprehensive Audio/Video Database of Domestic Falls of Elderly People

International audienceAmbient Assisted Living aims at enhancing the quality of life of older and disabled people at home thanks to Smart Homes. In particular, regarding elderly living alone at home, the detection of distress situation after a fall is very important to reassure this kind of population. However, many studies do not include tests in real settings, because data collection in this domain is very expensive and challenging and because of the few available data sets. The CIRDOcorpus is a dataset recorded in realistic conditions in DOMUS, a fully equipped Smart Home with microphones and home automation sensors, in which participants performed scenarios including real falls on a carpet and calls for help. These scenarios were elaborated thanks to a field study involving elderly persons. Experiments related in a first part to distress detection in real-time using audio and speech analysis and in a second part to fall detection using video analysis are presented. Results show the difficulty of the task. The database can be used as standardized database by researchers to evaluate and compare their systems for elderly person's assistance. Keywords: audio and video data set, multimodal corpus, natural language and multimodal interaction, Ambient Assisted Living (AAL), distress situation