A Deep Learning Approach for Work Related Stress Detection from Audio Streams in Cyber Physical Environments

Madhavi, Ishara; Chamishka, Sadil; Nawaratne, Rashmika; Nanayakkara, Vishaka; Alahakoon, Damminda; Silva, Daswin De

doi:10.1109/etfa46521.2020.9212098

Cited by 15 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the determination of parameter α, this paper determines the balance coefficient α according to the distribution of training samples. The specific calculation formula is shown in (15), where m i is the number of samples of the ith type samples.…”

Section: Periodic Focal Loss Functionmentioning

confidence: 99%

“…[14] combines algorithms commonly used in the field of natural language processing with key acoustic features in speech for acoustic depression recognition, and an improved F1 by 15%. [15] combines a CNN network with a Growing Self‐organizing Map (GSOM) to identify psychological stress states based on spectral features. [16] proposes a multi‐task TCN learning model to estimate the degree of depression, combined with related tasks such as emotion and emotion recognition to identify depression.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of depression state based on multi‐scale acoustic features in interrogation environment

Huang

Xiao

et al. 2023

IET Signal Processing

View full text Add to dashboard Cite

Depression diagnosis based on speech signals has the advantages of non-invasiveness, low cost, and few restrictions on portability. The research on the recognition of the depression state is carried out based on the acoustic information in the speech signal. Aiming at the interview dialogue speech in the consultation environment, a hierarchical attention temporal convolutional network (HATCN) acoustic depression recognition model is proposed. For sentence acoustic feature learning, a regional attention mechanism is introduced to extract multi-scale sentence features; for segment acoustic feature extraction, the traditional attention mechanism is used to calculate, which is in line with human cognitive mechanism. In addition, a periodic focal loss function is introduced to address the imbalance of positive and negative samples in depression diagnosis. Experiments show that the proposed acoustic depression recognition model has a certain improvement in recognition performance compared with other methods. At the same time, the influence of noise on the recognition of acoustic depression in the real consultation environment is analysed through experiments, and the data enhancement is carried out utilising speech noise, which proves the effectiveness of the data expansion of speech noise.

show abstract

Section: Periodic Focal Loss Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of depression state based on multi‐scale acoustic features in interrogation environment

Huang

Xiao

et al. 2023

IET Signal Processing

View full text Add to dashboard Cite

show abstract

“…The related studies attempt to find the suitability of pitch information in determining the emotions from audio segments. As Mel-scale spectrograms cause loss of pitch information, the research work in [27,36] better utilizes pitch information by extracting linearly spaced spectrogram features. It has been reported that the usage of statistical learning of the layers in a deep neural network for feature extraction, yields better results in contrast to the handcrafted low-level features [36].…”

Section: Low Performancementioning

confidence: 99%

A voice-based real-time emotion detection technique using recurrent neural network empowered feature modelling

Chamishka

Madhavi

Nawaratne

et al. 2022

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

The advancements of the Internet of Things (IoT) and voice-based multimedia applications have resulted in the generation of big data consisting of patterns, trends and associations capturing and representing many features of human behaviour. The latent representations of many aspects and the basis of human behaviour is naturally embedded within the expression of emotions found in human speech. This signifies the importance of mining audio data collected from human conversations for extracting human emotion. Ability to capture and represent human emotions will be an important feature in next-generation artificial intelligence, with the expectation of closer interaction with humans. Although the textual representations of human conversations have shown promising results for the extraction of emotions, the acoustic feature-based emotion detection from audio still lags behind in terms of accuracy. This paper proposes a novel approach for feature extraction consisting of Bag-of-Audio-Words (BoAW) based feature embeddings for conversational audio data. A Recurrent Neural Network (RNN) based state-of-the-art emotion detection model is proposed that captures the conversation-context and individual party states when making real-time categorical emotion predictions. The performance of the proposed approach and the model is evaluated using two benchmark datasets along with an empirical evaluation on real-time prediction capability. The proposed approach reported 60.87% weighted accuracy and 60.97% unweighted accuracy for six basic emotions for IEMOCAP dataset, significantly outperforming current state-of-the-art models.

show abstract

“…The proliferation of voice assistant chatbots such as Siri, Alexa, Cortana, and Google [ 34 ], as well as the numerous chatbot functions in online retail has familiarised a majority of modern society with the utility, engagement, and operation of a chatbot. For instance, in industrial settings, chatbots are used to provide information, instructions, detect fatigue, and address exceptions [ 35 , 36 ], while in healthcare, chatbots have been used for automated post-treatment communications and support groups, counselling, and healthcare service administrative support [ 37 , 38 ]. However, most chatbots are designed using Frequently Asked Questions (FAQs) for information provision or process specific in executing a well-defined repetitive or sequential series of tasks via conversational inputs.…”

Section: Related Workmentioning

confidence: 99%

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring

Rathnayaka

Mills

Burnett

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be scaled up to address the increasing demand from affected individuals, as evidenced in the first two years of the COVID-19 pandemic. Conversational agents, or chatbots, are a recent technological innovation that has been successfully adapted for mental healthcare as a scalable platform of cross-platform smartphone applications that provides first-level support for such individuals. Despite this disposition, mental health chatbots in the extant literature and practice are limited in terms of the therapy provided and the level of personalisation. For instance, most chatbots extend Cognitive Behavioural Therapy (CBT) into predefined conversational pathways that are generic and ineffective in recurrent use. In this paper, we postulate that Behavioural Activation (BA) therapy and Artificial Intelligence (AI) are more effectively materialised in a chatbot setting to provide recurrent emotional support, personalised assistance, and remote mental health monitoring. We present the design and development of our BA-based AI chatbot, followed by its participatory evaluation in a pilot study setting that confirmed its effectiveness in providing support for individuals with mental health issues.

show abstract

A Deep Learning Approach for Work Related Stress Detection from Audio Streams in Cyber Physical Environments

Cited by 15 publications

References 27 publications

Identification of depression state based on multi‐scale acoustic features in interrogation environment

Identification of depression state based on multi‐scale acoustic features in interrogation environment

A voice-based real-time emotion detection technique using recurrent neural network empowered feature modelling

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring

Contact Info

Product

Resources

About