arXiv AI
HEAR Who Said What: Unlocking Speaker-Attributed Reasoning via Counterfactual Voice Grounding
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arXiv AI
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Large Audio Language Models for Spoofing-Aware Speaker Verification
arXiv:2607. 14753v1 Announce Type: cross Abstract: Recent advances in text-to-speech and voice cloning make high-quality spoofing inexpensive and scalable, threatening voice authentication systems, especially automatic speaker verification (ASV).
arXiv AI
Aug 28
When Text Misleads: Inconsistent-Aware Reasoning for Audio-Grounded Dialogue
The paper introduces ContraTalk, a benchmark that tests whether dialogue models truly use acoustic cues or rely on transcript shortcuts. It formalizes cross‑modal disagreement, creates conflict and consistent QA examples, and proposes an Audio Twin representation to expose acoustic evidence to models. Experiments show that while text‑only LLMs perform well on consistent cases, they falter on conflict cases, and AudioLLMs only partially mitigate this issue.
arXiv AI
Jun 11
Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents
arXiv:2606. 11219v1 Announce Type: cross Abstract: Audio language models (ALMs) are increasingly used for speech-based understanding, yet their ability to perform semantic reasoning beyond transcription, Text-to-Audio Retrieval, Captioning, and Question-Answering accuracy remains insufficiently benchmarked.
arXiv AI
Jun 10
Linguistically Augmented Audio Speech Data (LinguAS)
arXiv:2606. 10246v1 Announce Type: cross Abstract: Maliciously-created fake speech, including deepfaked and spoofed audio, is proliferating at an alarming rate, and detection models are racing to stay ahead of the curve.
arXiv Computation and Language
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SURE-Voice: A Front-End Baseline for Speech-Evidence Filtering in Speech LLMs
arXiv:2608.27783v3 Announce Type: replace-cross Abstract: Speech language models (speech LLMs) can generate plausible outputs from audio that contains no usable speech evidence. We study this failure...
arXiv Machine Learning
Jul 27
Probing Speaker Identity Sensitivity in Audio Deepfake Detectors
arXiv:2607. 21820v1 Announce Type: cross Abstract: Audio deepfake detectors are trained to distinguish genuine speech from synthetic speech and often perform well on standard benchmarks.