arXiv AI June 30, 2026 By Chuxiao Zuo, Yao Zhu, Minqiang Xu, Manhong Wang, Yunke Zhang, Fei Huang

AMR: Adaptive Modality Routing for Multimodal Polyglot Speaker Identification

arXiv:2606. 29335v1 Announce Type: cross Abstract: Multimodal speaker identification systems face two key challenges in real-world deployment: missing modalities and language mismatch between training and testing conditions.

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 Computer Vision

4d ago

Efficient Audiovisual Speech Processing via MUTUD: Multimodal Training and Unimodal Deployment

arXiv:2501.18157v2 Announce Type: replace-cross Abstract: Building reliable speech systems often requires combining multiple modalities, like audio and visual cues. While such multimodal solutions fr...

By Joanna Hong, Sanjeel Parekh, Honglie Chen, Jacob Donley, Ke Tan, Buye Xu, Anurag Kumar

multimodal

arXiv Computation and Language

3d 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 25

Adaptive Fisher-Whitened Cross-Covariance for Low-Resource Speech Recognition

The paper introduces task-informed parameter-efficient fine-tuning methods for low-resource speech recognition by applying Fisher-Whitened Cross-Covariance Analysis (FCCA) to Whisper and Qwen3-ASR. Two extensions—Asymmetric-Coupled FCCA (AC‑FCCA) and Adaptive‑Rank FCCA (AR‑FCCA)—are proposed to exploit cross‑layer sharing and adapt rank allocation within a fixed parameter budget. Experiments on multilingual datasets show that standard FCCA matches or surpasses LoRA, while AR‑FCCA consistently improves performance across models without increasing trainable parameters.

By Asmee Mishra, Mengjie Qian, Brechtje Post, Kate Knill

fine-tuning multimodal

arXiv AI

Jun 2

VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio

arXiv:2512. 10120v2 Announce Type: replace-cross Abstract: General-purpose audio representations aim to map acoustically variable instances of the same event to nearby points, resolving content identity in a zero-shot setting.

By Maris Basha, Anja Zai, Sabine Stoll, Richard Hahnloser

rag multimodal benchmarks safety

arXiv Machine Learning

Jul 24

Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models

arXiv:2607. 21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern.

By Yingchao Huang, Xin Wang, Yuhan Su, Shanshan Yao

llms rag multimodal benchmarks safety

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