World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
6173
World Ranking
8922
National Ranking
543

Overview

Nicholas Cummins is affiliated with King's College London in the United Kingdom. Their research primarily focuses on the fields of psychology and medicine, with significant contributions in subfields such as experimental and cognitive psychology, applied psychology, cognitive neuroscience, clinical psychology, and artificial intelligence.

The areas of study they cover include mental health research topics, digital mental health interventions, emotion and mood recognition, speech recognition and synthesis, mental health via writing, COVID-19 and mental health, and speech and audio processing.

They have published extensively, with frequent appearances in venues such as arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), Zenodo (CERN European Organization for Nuclear Research), Research Square, and JMIR mhealth and uhealth.

Among their recent publications are:

  • Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study, 2021, JMIR mhealth and uhealth
  • Combining a parallel 2D CNN with a self-attention Dilated Residual Network for CTC-based discrete speech emotion recognition, 2021, Neural Networks
  • Five Crucial Challenges in Digital Health, 2020, Frontiers in Digital Health
  • Predicting Depressive Symptom Severity Through Individuals' Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study, 2021, JMIR mhealth and uhealth
  • Real-time assessment of COVID-19 prevalence among multiple sclerosis patients: a multicenter European study, 2020, Neurological Sciences

The scientist collaborates frequently with a group of coauthors including Richard Dobson, Matthew Hotopf, Amos Folarin, Vaibhav A. Narayan, and Faith Matcham. These collaborations have resulted in numerous joint publications.

Best Publications

  • A review of depression and suicide risk assessment using speech analysis

    Nicholas Cummins;Stefan Scherer;Jarek Krajewski;Sebastian Schnieder

  • AVEC 2017: Real-life Depression, and Affect Recognition Workshop and Challenge

    Fabien Ringeval;Björn Schuller;Michel Valstar;Jonathan Gratch

  • Snore Sound Classification Using Image-Based Deep Spectrum Features.

    Shahin Amiriparian;Maurice Gerczuk;Sandra Ottl;Nicholas Cummins

  • AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition

    Fabien Ringeval;Björn Schuller;Michel Valstar;Nicholas Cummins

  • An Investigation of Depressed Speech Detection: Features and Normalization.

    Nicholas Cummins;Julien Epps;Michael Breakspear;Roland Goecke

  • Using smartphones and wearable devices to monitor behavioural changes during COVID-19

    Shaoxiong Sun;Amos Folarin;Yatharth Ranjan;Zulqarnain Rashid

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

    Nicholas Cummins;Alice Baird;Björn W. Schuller;Björn W. Schuller

  • AVEC 2018 Workshop and Challenge: Bipolar Disorder and Cross-Cultural Affect Recognition

    Fabien Ringeval;Björn Schuller;Michel Valstar;Roddy Cowie

  • An Image-based Deep Spectrum Feature Representation for the Recognition of Emotional Speech

    Nicholas Cummins;Shahin Amiriparian;Gerhard Hagerer;Anton Batliner

  • Diagnosis of depression by behavioural signals: a multimodal approach

    Nicholas Cummins;Jyoti Joshi;Abhinav Dhall;Vidhyasaharan Sethu

  • auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks

    Michael Freitag;Shahin Amiriparian;Sergey Pugachevskiy;Nicholas Cummins

  • Analysis of acoustic space variability in speech affected by depression

    Nicholas Cummins;Vidhyasaharan Sethu;Julien Epps;Sebastian Schnieder

  • Exploring Deep Spectrum Representations via Attention-Based Recurrent and Convolutional Neural Networks for Speech Emotion Recognition

    Ziping Zhao;Zhongtian Bao;Yiqin Zhao;Zixing Zhang

  • Combining a parallel 2D CNN with a self-attention Dilated Residual Network for CTC-based discrete speech emotion recognition.

    Ziping Zhao;Qifei Li;Zixing Zhang;Nicholas Cummins

  • Automatic Assessment of Depression From Speech via a Hierarchical Attention Transfer Network and Attention Autoencoders

    Ziping Zhao;Zhongtian Bao;Zixing Zhang;Jun Deng

  • An Investigation of Annotation Delay Compensation and Output-Associative Fusion for Multimodal Continuous Emotion Prediction

    Zhaocheng Huang;Ting Dang;Nicholas Cummins;Brian Stasak

  • Five Crucial Challenges in Digital Health.

    Nicholas Cummins;Björn W. Schuller;Björn W. Schuller

  • Biosensors and Internet of Things in smart healthcare applications: challenges and opportunities

    Maria Pateraki;Konstantinos Fysarakis;Vangelis Sakkalis;Georgios Spanoudakis

  • Modeling Spectral Variability for the Classification of Depressed Speech

    Nicholas Cummins;Julien Epps;Vidhyasaharan Sethu;Michael Breakspear

  • Learning Image-based Representations for Heart Sound Classification

    Zhao Ren;Nicholas Cummins;Vedhas Pandit;Jing Han

  • Emotional expression in psychiatric conditions: New technology for clinicians

    Karol Grabowski;Agnieszka Rynkiewicz;Amandine Lassalle;Simon Baron-Cohen

  • Attention-Enhanced Connectionist Temporal Classification for Discrete Speech Emotion Recognition.

    Ziping Zhao;Zhongtian Bao;Zixing Zhang;Nicholas Cummins

Frequent Co-Authors

Björn Schuller
Björn Schuller Imperial College London
Julien Epps
Julien Epps University of New South Wales
Matthew Hotopf
Matthew Hotopf King's College London
Til Wykes
Til Wykes King's College London
Fabien Ringeval
Fabien Ringeval Grenoble Alpes University
Femke Lamers
Femke Lamers Amsterdam UMC
Maja Pantic
Maja Pantic Imperial College London
Roddy Cowie
Roddy Cowie Queen's University Belfast

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