SearcharxivSearch

arXiv · 2509.09479

Short-term cognitive fatigue of spatial selective attention after face-to-face conversations in virtual noisy environments

Abstract

Spatial selective attention is an important asset for communication in cocktail party situations but may be compromised by short-term cognitive fatigue. Here we tested whether an effortful conversation in a highly ecological setting depletes task performance in an auditory spatial selective attention task. Young participants with normal hearing performed the task before and after (1) having a real dyadic face-to-face conversation on a free topic in a virtual reverberant room with simulated interfering conversations and background babble noise at 72 dB SPL for 30 minutes, (2) passively listening to the interfering conversations and babble noise, or (3) having the conversation in quiet. Self-reported perceived effort and fatigue increased after conversations in noise and passive listening relative to the reports after conversations in quiet. In contrast to our expectations, response times in the attention task decreased, rather than increased, after conversation in noise and accuracy did not change systematically in any of the conditions on the group level. Unexpectedly, we observed strong training effects between the individual sessions in our within-subject design even after one hour of training on a different day.

Explore related subjects

Keep this discovery

BibTeXRIS

Ľuboš Hládek, Piotr Majdak, Robert Baumgartner. 2025-09-11. Short-term cognitive fatigue of spatial selective attention after face-to-face conversations in virtual noisy environments. https://arxiv.org/abs/2509.09479

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Diarization Error Decomposition Under Pause Annotation Ambiguity

Speaker diarization evaluation is sensitive to ambiguity in pause annotation, which can inflate diarization error rate (DER) or obscure genuine model errors. We show that morphological closing, which has been used for pause-tolerant diarization evaluation, discards segment-level distinctions. Instead, we propose an exact, overlap-aware decomposition of standard DER into a pause-attributable component, consisting of errors compatible with pause filling, and a residual core component that can serve as a proxy for intrinsic diarization errors. The decomposition leaves DER unchanged, while the pause-attributable and core components vary monotonically with the pause threshold and eventually saturate. Experiments spanning synthetic transformations, annotation mismatch, cross-domain evaluation, and tight-boundary diarization show that the decomposition reveals error sources not apparent from standard DER.

eess.AS

Less can be More: What Aspects of Speech Drive End-of-Turn Detection

In conversational AI, detecting when a speaker has finished talking is crucial for natural turn taking. While recent work incorporates semantics, the relative contribution of different modalities remains unclear. We present a controlled ablation of acoustic, prosodic, and semantic signals for streaming end of turn detection using a lightweight trimodal classifier. Under identical training conditions, the acoustic prosodic combination achieves the best balance of accuracy and latency, achieving utterance F1 of 0.93 with 7.8% false alarms at 400ms median latency. Adding text increases premature detections without improving performance. Feature space analysis confirms that prosodic features have the strongest class separability, while text representations overlap substantially. These findings suggest that turn-taking is primarily conveyed through intonation and silence patterns rather than semantic completeness, enabling faster and more reliable systems without expensive text inference.

eess.AS

Downstream-Task-Aware Unified Source Separation

Task-aware unified source separation (TUSS) enables a single model to handle diverse separation tasks by conditioning on input prompts. However, conventional TUSS does not account for downstream task requirements, such as whether the enhanced speech will be used for human listening or automatic speech recognition (ASR). In this paper, we propose a prompt extension framework for TUSS that incorporates downstream task information into the input prompts and switches the loss function according to the given prompt during training, enabling outputs with different signal characteristics at inference time. Specifically, we introduce an ASR-dedicated prompt paired with a regularized loss function that reduces speech artifacts to improve ASR robustness, while the standard prompt is paired with the conventional SNR loss function. Experiments on the LibriSpeech and JNAS corpora demonstrate that the proposed joint-training scheme enables a single model to improve ASR performance over noisy input across a wide range of SNR conditions by selecting the ASR-dedicated prompt, while maintaining general speech enhancement quality when the standard prompt is used.

eess.AS