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

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

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Computation and Language
Sep 23

Enriching Speech Emotion Representations with Conversational Context

The paper introduces ACERT, a module that incorporates a flexible-length window of conversational context to enhance Speech Emotion Recognition (SER). By capturing emotional evolution across utterances, ACERT outperforms state‑of‑the‑art methods on IEMOCAP, sets a new context‑aware benchmark on SAFE, and achieves strong results on MELD. Ablation studies attribute ACERT’s improvements to emotional and conversational continuity rather than speaker identity or acoustic conditions.

By Arthur Peuvot, Romaric Besan\c{c}on, Ga\"el de Chalendar, Bianca Vieru, Ioana Vasilescu
arXiv Machine Learning
Sep 7

Dual-Scale State-Space Modeling with Speaker-Wise Dynamic CRF for Speech Emotion Recognition in Conversation

The paper introduces DSSM-CRF, an audio‑only architecture for conversational speech emotion recognition that separates cross‑speaker contextual influence from within‑speaker emotion evolution. It uses bidirectional state‑space models to encode fused self‑supervised speech representations at both frame and dialogue scales, then orders each speaker’s utterances into an independent dynamic conditional random field chain. The model achieves state‑of‑the‑art performance on IEMOCAP and MELD, with complementary gains from speaker‑wise factorization and CRF modeling.

By Guan-Hua Wen, Hou-Chiang Tseng, Kuan-Yu Chen
arXiv AI
Jun 30

TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech

arXiv:2606. 30543v1 Announce Type: cross Abstract: With the proliferation of speech AI agents, understanding emotional entrainment in conversational interaction has become increasingly important.

By Sathvik Manikantan Napa Ugandhar, Hao Zhang, Alison Gunzler, Yuzhe Wang, Thomas Thebaud, Georgi Tinchev, Venkatesh Ravichandran, Laureano Moro-Vel\'azquez