arXiv:2606. 11260v1 Announce Type: cross Abstract: Humans process rich auditory environments through tightly integrated cognitive capabilities such as audio perception, audio reasoning, and memory.
arXiv:2609.22586v1 Announce Type: cross Abstract: Modern audio-language models are no longer judged only on what words they can transcribe, but on whether they can reason over what they hear: recover...
arXiv:2610.23745v1 Announce Type: cross Abstract: Recent large audio language models (LALMs) have extended their capabilities beyond lexical speech understanding to tasks requiring paralinguistic and...
arXiv:2606. 17417v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) achieve strong performance on a variety of audio understanding tasks but continue to struggle with temporal reasoning, a fundamental capability central to human auditory perception.
arXiv:2607. 11801v1 Announce Type: cross Abstract: Large audio-language models (LALMs) often underperform on fine-grained, non-semantic attributes of speech, such as a speaker's emotion, despite strong performance on speech content.
arXiv:2610.16942v1 Announce Type: cross Abstract: Large audio-language models (LALMs) are fast becoming a general interface to sound, yet how far their broad pretraining carries into specialized doma...
CoarseSoundNet is a deep learning framework designed to classify ecological soundscapes into biophony, geophony, and anthropophony under realistic passive acoustic monitoring conditions. The study systematically explores model architectures, training data composition, and evaluation strategies, finding that adding more domain‑similar data, a silence class, and post‑hoc calibration improves performance. An ecological case study shows that pre‑filtering recordings with CoarseSoundNet produces acoustic index trends similar to ground‑truth filtering, though the impact on avian alpha‑diversity relationships is modest.
arXiv:2606. 08194v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) integrate audio perception and language understanding within a unified framework, enabling a wide range of real-world applications.
SpeechCritic learns a diagnostic speech judge from only about 300 human-labeled comparisons, calibrating a frontier audio-language model with non-binding hints that select acoustic measurements agreeing with human judgments. The approach improves dimension-level agreement by 6.3 points and reduces mismatch in Tie rates by 10.4 points, and training a 7B judge with SFT, OPD, and RL yields distinct behaviors, with RL producing more specific rationales. The method works across language pairs, demonstrating a path from limited human preferences to a diagnostic speech judge.
SEA-SpeechBench is a large‑scale multitask benchmark for speech understanding in 11 Southeast Asian languages, comprising 97,194 samples across 99 evaluation sets and 597 hours of curated audio. It covers nine tasks in three categories—speech processing, paralinguistic analysis, and a novel temporal understanding dimension—using multilingual prompting in both native SEA languages and English. Evaluation of current models shows significant performance gaps, especially in temporal understanding, emotion recognition, and speech translation, with low‑resource languages lagging behind English by up to 41 percentage points.
The paper introduces AIB, the first benchmark of auditory perceptual illusion tasks designed for Large Audio Language Models (LALMs). It covers ten representative music, sound, and speech‑based illusory phenomena, each annotated for knowledge‑based priors, and evaluates models alongside controlled human listening studies. Results reveal that while LALMs are generally signal‑faithful on low‑level acoustic cues, some models show more human‑like responses when linguistic or musical priors are involved, yet none fully match human perceptual patterns.
arXiv:2606. 16084v1 Announce Type: new Abstract: Human language has often been described as combining structure at two levels: lower-level units combine into larger units, which then combine into larger sequences.