arXiv AI
Jun 10
Towards Robust Arabic Speech Emotion Recognition with Deep Learning
arXiv:2606. 10278v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) aims to identify a speaker's emotional state from audio signals.
Speech Emotion Recognition Using CNN and Its Use Case in Digital Healthcare
The paper presents a convolutional neural network (CNN) model for speech emotion recognition (SER), aiming to classify emotions from audio recordings. It evaluates the model using precision, recall, and F1 score, and explores how various input and output parameters influence performance. The study applies the SER system to digital healthcare, proposing it as a bridge between human emotional states and artificial intelligence.
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arXiv Machine Learning
5d ago
EmoNet: A Lightweight Attention-Based CNN for Speech Emotion Recognition
EmoNet is a lightweight attention‑based CNN framework designed for speech emotion recognition across seven categories. It combines a four‑block depthwise separable CNN encoder with squeeze‑and‑excitation attention, residual connections, a Bidirectional LSTM, and multi‑head attention pooling, using a 134‑channel acoustic feature set and data augmentation to enhance robustness. On a combined TESS, RAVDESS, and SAVEE dataset, EmoNet achieves 96.75% test accuracy with only 4.11 million parameters, outperforming a reproduced baseline and competing with larger state‑of‑the‑art models.
arXiv AI
Jul 21
Explainable Lightweight Compact Deep Models for Speech Emotion Recognition
arXiv:2607. 16803v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) is an important component in a wide range of human-centered applications, including healthcare, customer service, and human-omputer interaction.
arXiv AI
Jul 23
A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering
arXiv:2507. 07046v3 Announce Type: replace-cross Abstract: Nowadays, speech emotion recognition (SER) plays a vital role in the field of human-computer interaction (HCI) and the evolution of artificial intelligence (AI).
arXiv Computation and Language
Sep 1
VocalAffectBench: Evaluating Vocal Emotion Recognition in AI Audio Models
arXiv:2608.28932v1 Announce Type: new Abstract: Voice products increasingly need affective cues that are present in speech but absent from transcripts. We introduce VocalAffectBench, a public, test-o...
arXiv Machine Learning
Sep 15
Bridging the Gap in ECG-Based Emotion Recognition: A Unified Evaluation of Deep Learning Models
The paper evaluates deep learning models for electrocardiogram‑based emotion recognition, focusing on generalization across datasets rather than dataset‑specific performance. It introduces two open‑source tools—ARRC for standardized benchmarking and ARDT for inter‑dataset training—to merge three public AER datasets (CUADS, ASCERTAIN, DREAMER) into a more variable benchmark. Using these tools, the authors compare three prominent deep learning architectures and two CNN baselines with hyperparameter tuning and 10‑fold cross‑validation, revealing trade‑offs between accuracy and model complexity and providing a reproducible benchmark for future research.
arXiv Machine Learning
Jun 3
Speech Emotion Recognition using Attention-based LSTM-Network with Residual Connection
arXiv:2606. 03359v1 Announce Type: cross Abstract: Speech emotion recognition is an important component of modern human-computer interaction systems.
Hugging Face Trending Papers
Jun 2
Speech Emotion Recognition using Attention-based LSTM-Network with Residual Connection
Speech emotion recognition is an important component of modern human-computer interaction systems. However, many state-of-the-art approaches rely on large pretrained models with high computational and memory requirements, limiting their applicability.
arXiv Machine Learning
Jun 26
State-Specific Respiratory Signatures for Affective and Stress Recognition: Interpretable Respiratory Markers, Autocorrelation Lags, and Compact CNN Models
arXiv:2606. 26723v1 Announce Type: cross Abstract: Respiratory activity is a direct and interpretable physiological channel for wearable stress and affective-state recognition, yet many studies emphasize classification accuracy without identifying which respiratory properties separate different states.
arXiv AI
Oct 26
Interpretable Singing Emotion Recognition Using Acoustic and Vocal Technique Features
The paper introduces an interpretable framework for singing emotion recognition that uses a compact set of singing-specific descriptors covering prosody, spectral features, phonetics, and vocal technique. By applying SHAP and GRAD-CAM, the authors link model predictions to measurable acoustic phenomena, showing that vocal technique descriptors reduce cross-singer variability and provide interpretable insights. Out‑of‑domain tests on VocalSet reveal that vibrato and pharyngeal techniques are key emotional cues, with vibrato’s importance depending on articulation rate.
arXiv Computation and Language
Sep 4
SISER: Speaker-Invariant Speech Emotion Recognition with Entropy-Based Adversarial Training
SISER is a speaker‑invariant speech emotion recognition framework that combines wav2vec 2.0 for feature extraction with an ECAPA‑TDNN speaker discriminator in an entropy‑based adversarial training scheme. By leveraging self‑supervised representations, SISER reduces reliance on large labeled datasets and suppresses speaker identity more effectively than shallow classifiers. On the IEMOCAP benchmark, SISER achieves a UA of 60.63%, surpassing both the baseline (51.15%) and wav2vec 2.0 without speaker suppression (56.46%).
arXiv AI
Jun 8
Acoustic Cue Alignment in Audio Language Models for Speech Emotion Recognition
arXiv:2606. 07309v1 Announce Type: cross Abstract: Instruction-following audio language models (ALMs) can be augmented with explicit acoustic cues, yet it remains unclear whether such cues are used in a grounded way when the raw audio is already available.