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.
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).
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arXiv AI
Jul 28
EmotionAI: A Privacy-Preserving Computational Intelligence Pipeline for Speech-Emotion-Grounded Conversational Analysis
arXiv:2606. 24941v2 Announce Type: replace-cross Abstract: Reviewing recorded interviews for affective cues such as composure and agitation is slow and subjective, and cloud services that could automate the task require sensitive audio to leave the device.
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.
arXiv AI
Jun 29
Do Speech Emphasis Models Generalize across Languages and Emotions?
arXiv:2606. 27717v1 Announce Type: cross Abstract: Prosodic emphasis varies across languages, emotions, and speaking styles, yet existing emphasis detection models are largely trained and evaluated on monolingual neutral read speech.
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.
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 2
Sympatheia: Emotionally Adaptive Voice Assistant with Continuous Affect Conditioning
arXiv:2606. 00851v1 Announce Type: cross Abstract: Empathetic spoken dialogue systems must infer a user's emotional state to respond appropriately, yet everyday speech often carries weak, neutral, or ambiguous affective cues.
arXiv AI
Jun 11
MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation
arXiv:2606. 11197v1 Announce Type: cross Abstract: Speech-based automatic estimation of depression levels is essential for enabling early detection and timely intervention, particularly in resource-constrained mental health settings.
arXiv AI
Jul 15
HSEmotion Team at the 11th ABAW Challenge: Multi-Task Learning and Ambivalence/Hesitancy Video Recognition
arXiv:2607. 12774v1 Announce Type: cross Abstract: This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition.
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.
arXiv AI
Aug 7
A Study of ASR Adaptation and Representation Dimensionality Reduction in Persian Speech Emotion Recognition Using Whisper
arXiv:2608. 05165v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) in low-resource languages remains a challenging problem due to limited labeled data.
arXiv AI
Aug 11
IndexTTS 2.5 Technical Report
arXiv:2601. 03888v4 Announce Type: replace-cross Abstract: In prior work, we introduced IndexTTS 2, a zero-shot neural text-to-speech foundation model comprising two core components: a transformer-based Text-to-Semantic (T2S) module and a non-autoregressive Semantic-to-Mel (S2M) module, which together enable faithful emotion replication and establish the first autoregressive duration-controllable generative paradigm.