ASPIRE-SER: An Adaptive Acoustic Transformer Graph Framework with Calibrated Selective Prediction for Reliable Speech Emotion Recognition

An encoder–decoder transformer with signal-level data augmentation for robust speech emotion recognition

ASER: Attribute-Serialized Emotion Recognition With Generative Multi-Task Learning

Speech emotion recognition (SER) is commonly formulated as direct utterance-to-label classification, leaving the acoustic and semantic evidence behind each prediction implicit. This letter proposes Attribute-Serialized Emotion Recognition (ASER), an attribute-serialized generative multi-task framework for SER, which or...

From Speech to Editable Concepts: Probing Emotion Recognition with Concept Bottleneck Models

Speech emotion recognition (SER) is the task of assigning emotion labels to utterances. Early systems relied on acoustic features, whereas recent approaches combine multiple modalities, most commonly speech and text. Still, performance remains poor on many datasets. Large language models (LLMs) have therefore attracted...

EmoNet: A Lightweight Attention-Based CNN for Speech Emotion Recognition

Speech Emotion Recognition (SER) is a fundamental task in affective computing that enables applications such as mental health assessment, human-computer interaction, and automated customer support systems. Accurate emotion classification from speech remains challenging due to variations in speaker style, acoustic condi...

A novel deep hybrid parallel model of CNN- BiLSTM-Transformer for speech emotion recognition

Human speech carries not only words but also rich paralinguistic signals that reflect emotional states. Despite significant progress in automatic speech emotion recognition (SER), many existing models still struggle to fully exploit vocal information. In this paper, we propose a novel deep hybrid model with three paral...

A Reproducible Hybrid LSTM–SVM Framework for Speech Emotion Recognition Using Temporal–Acoustic Embeddings

Speech emotion recognition (SER) seeks to infer affective states from paralinguistic cues embedded in voice, but reliable evaluation is difficult because emotional speech is sequential, speaker-dependent, and sensitive to corpus and validation design. This study presents a hybrid Long Short-Term Memory–Support Vector M...