arXiv AI June 18, 2026 By Thomas Thebaud, Yuzhe Wang, Laureano Moro-Velazquez, Jesus Villalba-Lopez, Najim Dehak

Speaker Verification with Speech-Aware LLMs: Evaluation and Augmentation

arXiv:2603. 10827v2 Announce Type: replace-cross Abstract: Speech-aware large language models (LLMs) can accept speech inputs, yet their training objectives largely emphasize linguistic content or specific fields such as emotions or the speaker's gender, leaving it unclear whether they encode speaker identity.

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 AI.

arXiv AI

Jul 17

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).

By Sofya Savelyeva, Mariia Perunova, Evgeny Kushnir, Artem Dvirniak, Dmitrii Korzh, Oleg Y. Rogov

llms multimodal

arXiv Computation and Language

5d ago

Inference-Time Target Speaker Unlearning in LLM-Based Automatic Speech Recognition

The paper introduces a new target‑speaker unlearning task for automatic speech recognition (TSU‑ASR) that allows certain speakers to opt out of transcription while still indicating their presence. A lightweight Enrollment‑Conditioned Gating (ECG) module is added to a frozen dual‑stream speech LLM, enabling dynamic unlearning of new opt‑out speakers during inference. Experiments on AMI and AliMeeting datasets show significant drops in transcription accuracy for opt‑out speakers while preserving performance for retained speakers.

By Bo Su, Yueru Yan, Thai Le

llms multimodal safety

arXiv Computation and Language

2d ago

VOSSA: Voiceprint Optimization for Streaming Speech Architectures

arXiv:2609.38887v1 Announce Type: cross Abstract: Real-time voice conversion (VC) systems commonly rely on pretrained speaker embeddings from automatic speaker verification (ASV) models. While effect...

By Mu-Ruei Tseng, Waris Quamer, Ghady Nasrallah, Ricardo Gutierrez-Osuna

rag

arXiv Computation and Language

Sep 15

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...

By Mengzhe Geng

llms multimodal benchmarks

arXiv AI

Jun 3

Efficient ASR Training with Conversations that Never Happened

arXiv:2606. 03957v1 Announce Type: cross Abstract: Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data.

By M\'at\'e Gedeon, P\'eter Mihajlik

llms multimodal benchmarks

arXiv Computation and Language

Sep 23

Beyond Short Segments : Expanding Speaker Embeddings with Vector Archives

arXiv:2609.25007v1 Announce Type: cross Abstract: The performance of state-of-the-art speaker verification (SV) systems severely degrades on short utterances due to insufficient speaker-specific info...

By Hyunku Kang, Minkyu Cho, Chanwoo Kim

llms rag benchmarks

One email a morning, machine-written

Related stories

Large Audio Language Models for Spoofing-Aware Speaker Verification

Inference-Time Target Speaker Unlearning in LLM-Based Automatic Speech Recognition

VOSSA: Voiceprint Optimization for Streaming Speech Architectures

SURE-Voice: A Front-End Baseline for Speech-Evidence Filtering in Speech LLMs

Efficient ASR Training with Conversations that Never Happened

Beyond Short Segments : Expanding Speaker Embeddings with Vector Archives