arXiv:2609.05871v1 Announce Type: cross Abstract: Audio-conditioned language models often underuse acoustic cues such as prosody, emotion, and non-speech sounds, raising the question of whether ASR-s...
arXiv:2609.16551v1 Announce Type: new Abstract: Self-supervised learning (SSL) can reduce the need for labelled medical images, but the choice of pretext objective remains unclear for lung ultrasound...
arXiv:2607.12176v2 Announce Type: replace Abstract: Ambivalence and hesitancy (A/H) undermine digital behaviour-change interventions, and recognizing them automatically from video is the goal of the...
Self-supervised learning (SSL) can reduce the need for labelled medical images, but the choice of pretext objective remains unclear for lung ultrasound (LUS). Contrastive learning, masked reconstructi...
arXiv:2509.10452v3 Announce Type: replace-cross Abstract: Pretrained automatic speech recognition (ASR) models such as Whisper perform well but still need domain adaptation to handle unseen parlance....
arXiv:2609.39453v1 Announce Type: cross Abstract: Speech emotion recognition (SER) is the task of assigning emotion labels to utterances. Early systems relied on acoustic features, whereas recent app...
arXiv:2607. 04526v1 Announce Type: cross Abstract: First-shot anomalous sound detection in DCASE Challenge Task 2 must flag anomalies of unseen machine types with a single threshold, without knowing whether a test clip comes from the data-rich source domain (990 normal training clips) or the data-scarce target domain (10).
arXiv:2609.18533v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems exhibit unequal error rates across speaker groups, motivating interventions on their internal representation...
Automatic speech recognition (ASR) systems exhibit unequal error rates across speaker groups, motivating interventions on their internal representations. We ask whether speaker-linked attributes that...
arXiv:2609.21288v1 Announce Type: new Abstract: Surface electromyography (sEMG)-based silent speech interfaces are limited by cross-user variability and calibration burden. We study a limited-data se...
The paper introduces CUES, a lightweight heuristic for selecting encoder combinations in large audio‑language models by estimating complementarity through Pearson correlations of single‑encoder performance profiles. Using a frozen SmolLM2‑135M backbone, CUES consistently identifies optimal encoder sets for each track on the XARES‑LLM benchmark without requiring fusion training or test data. On broad audio tasks, CUES selects a diverse trio of encoders, improving performance by 4.3% over Whisper‑medium, while on text generation it opts for a focused speech‑only pair, outperforming mHuBERT‑147 by 6.3%. The results illustrate how correlation signals guide a diversity–interference trade‑off across different task families.
arXiv:2608.03854v4 Announce Type: replace Abstract: Quantized large language models can run on consumer hardware, which motivates interest in on-premises processing of sensitive data. The reliability...