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Found 56 papers, 21 papers with code
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.
Applying Cooperative Machine Learning to Speed Up the Annotation of Social Signals in Large Multi-modal Corpora
Scientific disciplines, such as Behavioural Psychology, Anthropology and recently Social Signal Processing are concerned with the systematic exploration of human behaviour.
auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks
auDeep is a Python toolkit for deep unsupervised representation learning from acoustic data.
Sound Audio and Speech Processing
Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
Automatic understanding of human affect using visual signals is of great importance in everyday human-machine interactions.
Accurate Emotion Strength Assessment for Seen and Unseen Speech Based on Data-Driven Deep Learning
In this paper, we propose a data-driven deep learning model, i. e. StrengthNet, to improve the generalization of emotion strength assessment for seen and unseen speech.
N-HANS: Introducing the Augsburg Neuro-Holistic Audio-eNhancement System
N-HANS is a Python toolkit for in-the-wild audio enhancement, including speech, music, and general audio denoising, separation, and selective noise or source suppression.
Sound Audio and Speech Processing
EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation
We introduce EGIC, an enhanced generative image compression method that allows traversing the distortion-perception curve efficiently from a single model.
Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data
Traditional convolutional layers extract features from patches of data by applying a non-linearity on an affine function of the input.
EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition
The corpus is then utilised to create a novel framework for multi-corpus speech emotion recognition, namely EmoNet.
The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts
ExVo 2022, includes three competition tracks using a large-scale dataset of 59, 201 vocalizations from 1, 702 speakers.
The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expression
The ACII Affective Vocal Bursts Workshop & Competition is focused on understanding multiple affective dimensions of vocal bursts: laughs, gasps, cries, screams, and many other non-linguistic vocalizations central to the expression of emotion and to human communication more generally.
Nkululeko: A Tool For Rapid Speaker Characteristics Detection
We present advancements with a software tool called Nkululeko, that lets users perform (semi-) supervised machine learning experiments in the speaker characteristics domain.
Poisson CNN: Convolutional neural networks for the solution of the Poisson equation on a Cartesian mesh
The Poisson equation is commonly encountered in engineering, for instance in computational fluid dynamics (CFD) where it is needed to compute corrections to the pressure field to ensure the incompressibility of the velocity field.
Calibrated Prediction Intervals for Neural Network Regressors
Despite their obvious aforementioned advantage in relation to accuracy, contemporary neural networks can, generally, be regarded as poorly calibrated and as such do not produce reliable output probability estimates.
A Machine Learning Framework for Automatic Prediction of Human Semen Motility
Finally, a linear SVR is trained on this feature representation.
Personalized Federated Deep Learning for Pain Estimation From Face Images
A potential approach to tackling this is Federated Learning (FL), which enables multiple parties to collaboratively learn a shared prediction model by using parameters of locally trained models while keeping raw training data locally.
Fast Single-Class Classification and the Principle of Logit Separation
Our experiments show that indeed in almost all cases, losses that are aligned with the Principle of Logit Separation obtain at least 20% relative accuracy improvement in the SLC task compared to losses that are not aligned with it, and sometimes considerably more.
The Many-to-Many Mapping Between the Concordance Correlation Coefficient and the Mean Square Error
Despite its drawbacks, $MSE$ is one of the most popular performance metrics (and a loss function); along with lately $\rho_c$ in many of the sequence prediction challenges.
Data Augmentation for Dementia Detection in Spoken Language
In this work, we investigate data augmentation techniques for the task of AD detection and perform an empirical evaluation of the different approaches on two kinds of models for both the text and audio domains.
Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker Verification
The performance of speaker-related systems usually degrades heavily in practical applications largely due to the presence of background noise.
An Enhanced Adversarial Network with Combined Latent Features for Spatio-Temporal Facial Affect Estimation in the Wild
This paper addresses these shortcomings by proposing a novel model that efficiently extracts both spatial and temporal features of the data by means of its enhanced temporal modelling based on latent features.
audEERING's approach to the One-Minute-Gradual Emotion Challenge
This paper describes audEERING's submissions as well as additional evaluations for the One-Minute-Gradual (OMG) emotion recognition challenge.
Weakly Supervised One-Shot Detection with Attention Similarity Networks
Neural network models that are not conditioned on class identities were shown to facilitate knowledge transfer between classes and to be well-suited for one-shot learning tasks.
Learning audio sequence representations for acoustic event classification
Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC.
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Deep Structured Learning for Facial Action Unit Intensity Estimation
The goal of this paper is to model these structures and estimate complex feature representations simultaneously by combining conditional random field (CRF) encoded AU dependencies with deep learning.
Tunable Sensitivity to Large Errors in Neural Network Training
We propose incorporating this idea of tunable sensitivity for hard examples in neural network learning, using a new generalization of the cross-entropy gradient step, which can be used in place of the gradient in any gradient-based training method.
Detecting Road Surface Wetness from Audio: A Deep Learning Approach
We introduce a recurrent neural network architecture for automated road surface wetness detection from audio of tire-surface interaction.
A Broadcast News Corpus for Evaluation and Tuning of German LVCSR Systems
Transcription of broadcast news is an interesting and challenging application for large-vocabulary continuous speech recognition (LVCSR).
Acoustic Gait-based Person Identification using Hidden Markov Models
The goal of the system is to analyse sounds emitted by walking persons (mostly the step sounds) and identify those persons.
The state of play of ASC-Inclusion: An Integrated Internet-Based Environment for Social Inclusion of Children with Autism Spectrum Conditions
Individuals with Autism Spectrum Conditions (ASC) have marked difficulties using verbal and non-verbal communication for social interaction.
6th International Symposium on Attention in Cognitive Systems 2013
This volume contains the papers accepted at the 6th International Symposium on Attention in Cognitive Systems (ISACS 2013), held in Beijing, August 5, 2013.
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains.
Scaling Speech Enhancement in Unseen Environments with Noise Embeddings
We address the problem of speech enhancement generalisation to unseen environments by performing two manipulations.
The Principle of Logit Separation
In contrast, there are known loss functions, as well as novel batch loss functions that we propose, which are aligned with this principle.
Voice command generation using Progressive Wavegans
Generative Adversarial Networks (GANs) have become exceedingly popular in a wide range of data-driven research fields, due in part to their success in image generation.
Synthesising 3D Facial Motion from "In-the-Wild" Speech
Synthesising 3D facial motion from speech is a crucial problem manifesting in a multitude of applications such as computer games and movies.
Single-Channel Speech Separation with Auxiliary Speaker Embeddings
We present a novel source separation model to decompose asingle-channel speech signal into two speech segments belonging to two different speakers.
EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings
Motivated by this, we propose a novel crossmodal emotion embedding framework called EmoBed, which aims to leverage the knowledge from other auxiliary modalities to improve the performance of an emotion recognition system at hand.
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.
On Laughter and Speech-Laugh, Based on Observations of Child-Robot Interaction
In this article, we study laughter found in child-robot interaction where it had not been prompted intentionally.
Adversarial-based neural networks for affect estimations in the wild
However, the use of latent features, which is feasible through adversarial learning, is not largely explored, yet.
Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data
However, the model can become redundant if it is intended for a specific task.
Cross-lingual Zero- and Few-shot Hate Speech Detection Utilising Frozen Transformer Language Models and AXEL
Detecting hate speech, especially in low-resource languages, is a non-trivial challenge.
An Overview on Audio, Signal, Speech, & Language Processing for COVID-19
Recently, there has been an increased attention towards innovating, enhancing, building, and deploying applications of speech signal processing for providing assistance and relief to human mankind from the Coronavirus (COVID-19) pandemic.
Computers and Society Sound Audio and Speech Processing
The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements
Truly real-life data presents a strong, but exciting challenge for sentiment and emotion research.
Fitbeat: COVID-19 Estimation based on Wristband Heart Rate
This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection using remotely collected heart-rate data.
An Estimation of Online Video User Engagement from Features of Continuous Emotions
We investigate features extracted from these signals against various user engagement indicators including views, like/dislike ratio, as well as the sentiment of comments.
Proceedings of the ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts
The first, ExVo-MultiTask, requires participants to train a multi-task model to recognize expressed emotions and demographic traits from vocal bursts.
A Comparative Cross Language View On Acted Databases Portraying Basic Emotions Utilising Machine Learning
Since several decades emotional databases have been recorded by various laboratories.
SyntAct: A Synthesized Database of Basic Emotions
Speech emotion recognition is in the focus of research since several decades and has many applications.
Proceedings of the ACII Affective Vocal Bursts Workshop and Competition 2022 (A-VB): Understanding a critically understudied modality of emotional expression
This is the Proceedings of the ACII Affective Vocal Bursts Workshop and Competition (A-VB).
Enhancing Speech Emotion Recognition Through Differentiable Architecture Search
In contrast to previous studies, we refrain from imposing constraints on the order of the layers for the CNN within the DARTS cell; instead, we allow DARTS to determine the optimal layer order autonomously.
Ranked #5 on
Speech Emotion Recognition
on IEMOCAP
(UA metric)
Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation
In speaker-independent speech emotion recognition, the training and testing samples are collected from diverse speakers, leading to a multi-domain shift challenge across the feature distributions of data from different speakers.
Speech Swin-Transformer: Exploring a Hierarchical Transformer with Shifted Windows for Speech Emotion Recognition
These segment-level patches are then encoded using a stack of Swin blocks, in which a local window Transformer is utilized to explore local inter-frame emotional information across frame patches of each segment patch.
