arXiv AI By Yu Zheng, Jinghan Peng, ChangHao Zhang, Jian Liu, Weiqiang Wang

MECT: Mixture of Experts with CNN-Transformer Network for Speaker verification

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arXiv AI
Jun 18

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.

By Thomas Thebaud, Yuzhe Wang, Laureano Moro-Velazquez, Jesus Villalba-Lopez, Najim Dehak
arXiv AI
Jul 28

cMoLLM at Scale: Horizontal Scaling Laws for Mixture-of-LLMs

arXiv:2607. 22577v1 Announce Type: new Abstract: Scaling large language models (LLMs) has driven their success, yet dense Transformers couple capacity and computation: every parameter is activated for every token, making training and inference costs grow linearly with model size-a critical bottleneck as models approach trillion-parameter regimes.

By Xin Yang, Yemin Wang, Mingda Liu, Letian Li, Shuaishuai Cao, Zhengxiao He, Ryan Dong
arXiv AI
Sep 7

SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection

The paper introduces SNAP, a speaker‑nulling framework designed to improve deepfake speech detection. By estimating a speaker subspace and orthogonally projecting out speaker‑dependent components, SNAP isolates synthesis artifacts in the residual features. This reduction of speaker entanglement enables detectors to focus on artifact‑related cues, achieving state‑of‑the‑art performance.

By Kyudan Jung, Jihwan Kim, Minwoo Lee, Soyoon Kim, Jeonghoon Kim, Jaegul Choo, Cheonbok Park