Non-Speech Audio Distractions

• Non-speech audio distractions are non-linguistic sounds that disrupt both human cognition and machine processing by masking target signals.
• Recent studies quantify their disruptive effects through energetic masking in humans and representational entanglement in ASR and LLMs, leading to performance deficits.
• Engineering mitigations such as dynamic range compression and multimodal safety guards have been developed to reduce these distractions and enhance system robustness.

Non-speech audio distractions are non-linguistic acoustic signals that interfere with the processing of target information—be it for humans (e.g., speech intelligibility, task accuracy, well-being) or for artificial systems (e.g., perception models, safety filters, multimodal LLMs). These can include ambient noises, mechanical and biological sounds, musical backgrounds, white noise, and intentional adversarial distractors. Their disruptive effect arises from energetic and informational masking in humans, and from representational entanglement or task confusion in machines. Recent work rigorously quantifies their perceptual, behavioral, and algorithmic impacts, and proposes engineering and algorithmic mitigations specialized for audio LLMs (LALMs), automatic speech recognition (ASR), neurodivergent populations, and security-critical human-computer interaction.

1. Taxonomy and Characterization of Non-Speech Audio Distractions

A comprehensive taxonomy distinguishes non-speech audio distractions along several axes:

• Source/Type: Environmental (alarms, vehicle sounds), biological (chewing, breathing, sniffling), mechanical (typing, door squeaks), synthetic (white/pink noise or silence), and musical (instrumental, lyrical) (Ammari et al., 19 Jan 2026, Popescu et al., 12 Sep 2025, Scharenborg et al., 2018).
• Acoustic Features: Temporal (continuous/irregular vs. discrete/regular), spectral (wideband noise, tonal events), and intensity (measured SPL, SNR relative to speech) (Kaczmarek et al., 2014, Scharenborg et al., 2018).
• Intentionality: Accidental (background sounds), user-generated (remote conference chewing), or adversarial (jailbreak attacks with contextual audio) (Yang et al., 13 Nov 2025, Yang et al., 2024).
• Physiological and Psychological Impact: Trigger sounds for misophonia (chewing, pen clicking), ergonomic distractors (alarms, sirens), and masking agents in communication (Ammari et al., 19 Jan 2026, Popescu et al., 12 Sep 2025, Scharenborg et al., 2018).

In human studies, non-speech triggers induce involuntary physiological arousal—sweating, heart rate increase, nausea—especially in misophonia (Ammari et al., 19 Jan 2026). For LALMs and ASR systems, non-speech distractors cause hallucinations, task confusion, or safety filter bypass (Barański et al., 20 Jan 2025, Yang et al., 2024, Yang et al., 13 Nov 2025).

2. Psychophysical and Behavioral Impact on Humans

Non-speech audio distractors impact humans primarily by energetic masking (loss of audibility due to spectral or temporal overlap), and informational masking (cognitive interference from competing linguistic or structured content):

• Masking Effects: Background instrumental music (energetic maskers) and lyrical music (informational maskers) differentially impair word recognition, with lyrics causing a significant drop in accuracy at moderate and low SNRs. For example, at 0 dB SNR, recognition drops from ~0.70 with music-only to ~0.54 with lyrics (Scharenborg et al., 2018).
• Complexity Dependency: Highly percussive, unpredictable music increases masking, but the informational penalty of lyrics is additive, independent of rhythmic complexity (Scharenborg et al., 2018).
• Misophonia and Neurodivergence: Individuals with misophonia or neurodivergent profiles report high distress and avoidance behaviors in response to repetitive, ambient, or biological non-speech sounds (e.g., 81% of affected users log off platforms when exposed) (Ammari et al., 19 Jan 2026, Popescu et al., 12 Sep 2025).
• Task Distraction and Facilitation: Non-speech distractions, both accidental (environmental) and intended (experimentally administered), can unpredictably influence human error rates in security-critical tasks: some studies found lowered failure rates but unchanged task completion times (Kaczmarek et al., 2014).

A plausible implication is that non-speech audio distractions can both directly mask linguistic information and induce aversive physiological responses, contributing to task avoidance or performance deficits.

3. Failure Modes in Automatic Speech Recognition and Audio-LLMs

Deep neural ASR and multimodal LLMs display several characteristic failure modes under non-speech audio distraction:

• ASR Hallucinations: Whisper ASR produces high rates of "hallucinated" transcripts in response to pure non-speech audio—40.3% overall, with over 50% of outputs being a small set of frequent phrases (e.g., "thank you", "thanks for watching"). These reflect training data priors and LLM bias, not acoustic evidence (Barański et al., 20 Jan 2025).
• Looping and Spurious Output: With longer or overlapped non-speech input (>30 s or high-level noise), hallucination rates and text fragment looping increase (Barański et al., 20 Jan 2025).
• Cross-modal Entanglement: LALMs (e.g., Qwen2-Audio, Kimi-Audio) experience accuracy and mean average precision drops of 10–17 points when forced to process joint ASR, scene, and event tasks under mixed speech/non-speech conditions, particularly when SNR is low (Yin et al., 16 Sep 2025).
• Safety Filter Bypass in LMMs: Non-speech noise (synthetic or contextual) can collapse decision boundaries in joint audio-text representation space, leading to a doubling or tripling of attack success rates (e.g., white noise raises dangerous output rates from ~20% to >60%) (Yang et al., 2024, Yang et al., 13 Nov 2025).

The underlying mechanism is representational drift in high-dimensional embedding space and insufficient training on mixed-modality noise, which leads to models "believing" in spurious inputs or losing the ability to separate modalities.

4. Adversarial and Compositional Attack Vectors

Non-speech distractors are potent vectors for adversarial attacks and model jailbreaks:

• Speech-Audio Compositional Attacks: SACRED-Bench demonstrates that overlaying harmful non-speech contextual audio at an attenuation (–6 to –10 dB relative to speech) with benign speech results in extremely high attack success rates—even for Gemini 2.5 Pro (88.56% for speech-audio mixtures, overall 66.75%) (Yang et al., 13 Nov 2025).
• Noise-Only and Contextual Jailbreaks: Feeding Gaussian noise (origin- or standard-distributed) as the audio channel along with a harmful text query destabilizes defense heads and increases attack success by 40+ points relative to text-only (Yang et al., 2024).
• Environmental and Social Implications: The most effective composition attacks use environmental noise, structured sound events, or multi-speaker dialogue to evade or confuse safeguards designed for transcribed text.

Findings indicate that even state-of-the-art proprietary and open-source models are highly vulnerable to these attack vectors unless specifically trained or equipped with multimodal safety guards.

5. Engineering Mitigations: Detection, Filtering, and Design Strategies

Mitigation strategies span front-end detection and filtering, multimodal safety fusion, and user-centered design:

• Hallucination Suppression in ASR: Combining "Bag of Hallucinations" (BoH) filtering (Aho–Corasick search and forced alignment) and looping removal reduces non-speech-induced hallucination rates from >20% to 0%, with WER in speech+noise dropping from >100% to ~17% (or <10% if VAD is added) (Barański et al., 20 Jan 2025).
• Chain-of-Thought Reasoning: Step-wise processing (energy-onset → ASR → scene/event → consistency check) in LALMs recovers up to 4–5 points in mean accuracy for joint tasks under distraction (Yin et al., 16 Sep 2025).
• Dynamic Range Compression (DRC) for Trigger Attenuation: For neurodivergent users, single-band DRC with appropriate thresholds and attack/release times robustly attenuates distressing non-speech sounds, lowering subjective distress by ~28 points (on a 0–100 scale) while maintaining low algorithmic latency (1 sample delay) (Popescu et al., 12 Sep 2025).
• Real-time Trigger Detection and Gain Control: On-device classifiers (random forest, 1-D CNN with MFCC/StFT features) and frame-wise gain reduction enable per-user, per-category non-speech trigger filtering with a 82% decrease in distress in pilot studies (Ammari et al., 19 Jan 2026).
• User and Organizational Controls: VC platform integrations—offering per-source sliders, channel separation, group preference dashboards—improve participation and comfort for users sensitive to non-speech distractions (Ammari et al., 19 Jan 2026).
• Multimodal Safety Guards: Lightweight models (e.g., SALMONN-Guard) that jointly inspect speech, audio, and text embeddings reduce attack success by ~80% on SACRED-Bench and maintain 100% accuracy on benign tasks, outperforming ASR→text-only pipelines (Yang et al., 13 Nov 2025).

Recommendations include audio gating, SNR checks, curriculum training with non-speech noise, energy-based segmentation, and explicit multimodal alignment during training and inference (Yang et al., 2024, Yin et al., 16 Sep 2025).

6. Evaluation Methodologies and Benchmarks

A range of benchmarks and methods have been proposed to systematically test the impact of non-speech audio distractions:

Benchmark	Task Types	Composition/Distraction Modes
SSEU-Bench (Yin et al., 16 Sep 2025)	ASR, Scene, Event (ind./joint)	Speech + background event/scene at SNR grid
SACRED-Bench (Yang et al., 13 Nov 2025)	Safety/Jailbreak (bin./open QA)	Speech+harmful audio, dialogue, overlap

These enable controlled manipulation of SNR, event type, and mixture mechanism, and support evaluation of accuracy, mAP, WER, and attack success rates as a function of distraction strength and model architecture.

7. Prospects and Open Challenges

Future directions include:

• Developing generalizable multimodal defense heads that disentangle and correctly interpret speech vs. non-speech even under high overlap and intentional distraction.
• Improving real-time, low-latency detection and attenuation for consumer and clinical populations with tight power and complexity budgets (Popescu et al., 12 Sep 2025).
• Expanding taxonomies and benchmark coverage for rare, rapidly co-occurring, or intentionally adversarial sound events (Gong et al., 2023, Barański et al., 20 Jan 2025).
• Addressing the balance between robust audio understanding and sensitivity to potentially relevant but distressing background content (e.g., in surveillance or accessibility applications).
• Integrating shared user preference profiles and participatory design to accommodate sensory diversity, especially in remote collaboration platforms (Ammari et al., 19 Jan 2026).

Rigorous psychophysical, algorithmic, and engineering research continues to reveal the subtle yet profound ways non-speech audio distractions shape the performance, safety, and inclusivity of human and machine audio processing systems.