arXiv:2511. 16757v2 Announce Type: replace-cross Abstract: Audio-language pretraining (ALP) holds promise for learning general-purpose audio representation, yet remains underexplored.
By Wei-Cheng Tseng, Xuanru Zhou, Mingyue Huo, Yiwen Shao, Hao Zhang, Dong Yu
arXiv:2606. 14591v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have shown strong performance on a wide range of audio understanding tasks, yet they still struggle with complex audio reasoning.
By Hui Geng, Yi Su, Han Yin, Tianjiao Wan, Qisheng Xu, Jiaxin Chen, Zijian Gao, Hengzhu Liu, Xie Chen, Kele Xu
VoiceTrace introduces a new benchmark, VoiceTrace-Bench, for hybrid speech retrieval that combines a textual query specifying "what" to retrieve with a reference speech specifying "who" to retrieve. The authors propose a two‑stage framework: VoiceTrace‑Emb, which learns unified audio‑text embeddings for efficient large‑scale retrieval, and VoiceTrace‑Reranker, which fine‑grains relevance by jointly examining query‑candidate pairs. Experiments show VoiceTrace outperforms existing methods on both traditional semantic speech retrieval benchmarks and the new hybrid setting.
By Aaron Yee, Fengjie Lu, Jiarui Hai, Chenang Jiang, Helin Wang, Siwei Tu, Weitao You, Lingyun Sun
SonicCaps is a large-scale audio captioning dataset featuring approximately 15 million captions paired with 700,000 audio clips, created using the Qwen3-Omni multimodal language model. The dataset emphasizes diversity by generating around 24 captions per clip through structured prompt engineering and few-shot generation, covering main descriptions, rephrased variants, and semantic tags. Human evaluations rate SonicCaps higher than existing datasets, and training CLAP models on it improves audio retrieval and zero-shot classification across public and commercial benchmarks.
By Zineb Lahrichi, Marc Ferras, Ga\"el Richard, Geoffroy Peeters
The paper introduces Omni-Interactive Universal Embedder (OmniUE), a unified embedding framework that learns a single representation space for text, video, and audio using learnable tokens and intermediate-layer representations. OmniUE supports omni-interactive querying, allowing users to input text, visual regions, or audio spans, which are processed by segmenters and an omni-LLM to generate user-conditioned embeddings. The authors evaluate OmniUE on the new OmniCHOIR benchmark and other multimodal tasks, reporting significant performance gains over state‑of‑the‑art baselines across textual, audio, and visual interactive settings.
By Wei-Yao Wang, Kazuya Tateishi, Shuyang Cui, Christian Simon, Takashi Shibuya, Shusuke Takahashi, Yuki Mitsufuji
EXAM$^2$ is a new benchmark for audio understanding that covers six languages and multiple modalities—speech, sound, music, mixed-audio, and visual images—providing 5,667 multiple-choice questions, 22,614 image instances, and 135,684 multilingual translations. It evaluates large audio language models (LALMs) and multimodal large language models (LLMs), revealing significant gaps in multilingual and cross‑modal performance. The authors also introduce Gemma3n-EXAM$^2$, a lightweight fusion model that improves multilingual results by up to 12.4% and multimodal results by 21.7% over a strong baseline.
By Jiawen Wang, Xiaoxue Gao, Zi Haur Pang, Nancy F. Chen