arXiv AI

ProSarc: Prosody-Aware Sarcasm Recognition Framework via Temporal Prosodic Incongruity

arXiv:2606. 06168v1 Announce Type: new Abstract: We present ProSarc, an audio-only framework that detects sarcasm by modelling temporal prosodic incongruity, that is, the mismatch between local prosodic dynamics and the utterance-level emotional baseline.

arXiv AI

Jun 16

NVMOS: Non-Verbal Vocalization Quality Assessment in Speech

arXiv:2606. 15888v1 Announce Type: cross Abstract: Non-verbal vocalizations (NVs), such as laughter, sighs, and coughs, are important acoustic cues for emotion and intent.

By Jialong Mai, Jinxin Ji, Xiaofen Xing, Wencui Liu, Xiangmin Xu

llms multimodal

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.

By Megan Wei, Deepali Aneja, Jiaqi Su, Yunyun Wang, Haonan Chen, Zeyu Jin

benchmarks

Hugging Face Trending Papers

Jun 23

ParaPairAudioBench: Paralinguistic Pairwise Audio Benchmark for LALM-as-a-Judge

Large Audio-Language Models (LALMs) have been widely used as judge models for the automatic evaluation of generated speech. However, prior approaches predominantly focus on holistic naturalness, leaving fine-grained paralinguistic distinctions underexplored.

llms benchmarks

arXiv AI

Jun 16

Robust Dual-Signal Fusion: Hybrid Neuro-Symbolic Gating with Compressed Chain-of-Thought Refinement for Irony Detection in Social Media Texts

arXiv:2606. 16845v1 Announce Type: cross Abstract: Large Language Models (LLMs) natively default to literal semantic interpretations, making zero-shot irony detection a persistent challenge.

By Ankit Bhattacharjee, Krityapriya Bhaumik

llms fine-tuning benchmarks safety

arXiv AI

Jul 9

Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts

arXiv:2607. 06611v1 Announce Type: cross Abstract: Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and the interpretation of uttered words.

By Andrei-George Durdun, Victor Constantinescu, Radu Tudor Ionescu

llms nlp efficiency multimodal benchmarks

arXiv Machine Learning

Jul 2

Automatic Detection of Stress from Speech in the Trier Social Stress Test

arXiv:2607. 00986v1 Announce Type: new Abstract: Automatically detecting stress in speech provides an unobtrusive way to gain insights relevant to behavioral research or clinical assessment.

By Hanna Drimalla, Wieland R. Cremer, Christine Kraus, Oliver T. Wolf

arXiv AI

Jun 2

Beyond the Mouth: Upper-Face Affective Cues in Audiovisual Sentence Recognition under Acoustic Uncertainty

arXiv:2606. 00670v1 Announce Type: cross Abstract: Face-to-face speech comprehension is inherently multimodal, integrating acoustic signals with visible articulation, facial expression, head motion, and other socially relevant cues.

By Zhou Yang, Yueyi Yang

multimodal

arXiv Machine Learning

Jun 9

Titans-as-a-Layer: Test-Time Memory for Conversational Speech Emotion Recognition

arXiv:2606. 08573v1 Announce Type: new Abstract: Speech emotion recognition (SER) is commonly formulated as utterance-level classification, although conversational emotion depends on a speaker's usual vocal range and the emotional context established by previous utterances.

By Daniel Chen, Qicong Hu, Yang Xiao, Ting Dang, Hong Jia

llms

arXiv AI

Jul 28

StanceBench: A Benchmark for Audio LLM-Based Interpersonal Stance Evaluation from Speech

arXiv:2607. 22658v1 Announce Type: new Abstract: Speech-to-speech dialogue models increasingly depend on prosody and interactional nuance to convey social intent, yet benchmarks for these cues remain limited.

By Yuzhe Wang (Electrical and Computer Engineering Department, Johns Hopkins University, Baltimore, USA), Thomas Thebaud (Electrical and Computer Engineering Department, Johns Hopkins University, Baltimore, USA), Jennifer Hu (Department of Cognitive Science, Johns Hopkins University, Baltimore, USA), Jes\'us Villalba-Lopez (Electrical and Computer Engineering Department, Johns Hopkins University, Baltimore, USA), Venkatesh Ravichandran (Amazon AGI, USA), Georgi Tinchev (Amazon Research, UK), Najim Dehak (Electrical and Computer Engineering Department, Johns Hopkins University, Baltimore, USA), Laureano Moro-Vel\'azquez (Electrical and Computer Engineering Department, Johns Hopkins University, Baltimore, USA)

llms benchmarks safety

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).

By Shahana Yasmin Chowdhury, Bithi Banik, Md Tamjidul Hoque, Shreya Banerjee

benchmarks

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.

By Iosif Tsangko, Andreas Triantafyllopoulos, Bj\"orn W. Schuller

llms benchmarks safety

arXiv AI

2d ago

When AI Rewrites, Classifiers Relax: Uncertainty-Aware Sentiment Analysis on Sarcastic and AI-Paraphrased Social Text

arXiv:2608. 15338v1 Announce Type: cross Abstract: Sentiment classifiers are increasingly applied to social media content that is either sarcastic or AI-generated --- two distributional regimes where standard evaluations offer little guidance.

By Shresth Shroff

nlp safety

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