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20152026
most citedASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks

18 citations · 59 across the 53 of their papers we have counts for

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12 papers · 1 filter

cs.MA2026

Who Are They to Each Other? Multi-Agent Reasoning for Speaker Relationship Inference

Yaohan Guan, Yen-Ju Lu, Yuzhe Wang +5

Inferring speaker relationships from spoken conversations is an important step towards socially aware speech understanding. However, this task remains underexplored, and supervised…

cs.CL2026

When Text Misleads: Inconsistent-Aware Reasoning for Audio-Grounded Dialogue

Yen-Ju Lu, Yuzhe Wang, Yaohan Guan +8

Understanding spoken dialogue requires joint reasoning over lexical content and paralinguistic acoustic signals such as emotion and conversational intent. However, existing evaluat…

eess.AS2026

Leveraging Gradient Reversal Loss and Multitask Learning for Datasets-Aware Audio Deepfake Detection

Mingrui Liang, Thomas Thebaud, Lukasz Wojciak +4

Recent advances in speech synthesis and voice conversion, which pose threats to security and privacy, have underscored the need for deepfake detection technology. Although existing…

eess.AS2026

ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions

Thomas Thebaud, Junhyeok Lee, Laureano Moro-Velazquez +2

Speaker embeddings, or x-vectors, are widely used to represent speaker identity and speaker-related attributes, but existing embedding extractors are typically descriptive rather t…

cs.AI2026

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

Yuzhe Wang, Thomas Thebaud, Jennifer Hu +5

Speech-to-speech dialogue models increasingly depend on prosody and interactional nuance to convey social intent, yet benchmarks for these cues remain limited. We introduce StanceB…

cs.SD2026

Clustering Unsupervised Representations as Defense against Poisoning Attacks on Speech Commands Classification System

Thomas Thebaud, Sonal Joshi, Henry Li +4

Poisoning attacks entail attackers intentionally tampering with training data. In this paper, we consider a dirty-label poisoning attack scenario on a speech commands classificatio…