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ARXIV:2605.15984 · SPEECH TOXICITY DETECTION · SUBMITTED 18 MAY · 20:28 UTC · FRESHNESS STALE
ARXIV:2605.15984SPEECH TOXICITY DETECTIONSUBMITTED 18 MAY · 20:28 UTCFRESHNESS STALEZhongjie Ba · Liang Yi · Peng Cheng · Qingcao Li · Qinglong Wang · Li Lu · arXiv
ToxiAlert-Bench is a comprehensive audio dataset and a dual-head neural network framework that detects toxic speech by incorporating paralinguistic cues, significantly improving accuracy over text-based methods.
Opportunity summary
Pain ToxiAlert-Bench is a comprehensive audio dataset and a dual-head neural network framework that detects toxic speech by incorporating paralinguistic cues, significantly improving accuracy over text-based methods.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
ToxiAlert-Bench is a comprehensive audio dataset and a dual-head neural network framework that detects toxic speech by incorporating paralinguistic cues, significantly improving accuracy over text-based methods. However, existing approaches to toxic speech detection often…
Toxic speech detection has become a crucial challenge in maintaining safe online communication environments. However, existing approaches to toxic speech detection often neglect the contribution of paralinguistic cues, such as emotion, intonation, and speech…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To mitigate data class imbalance, we incorporate class-balanced sampling and weighted loss functions.Our experimental results show that leveraging paralinguistic features significantly improves detection performance.…
Speech Toxicity Detection moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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ToxiAlert-Bench is a comprehensive audio dataset and a dual-head neural network framework that detects toxic speech by incorporating paralinguistic cues, significantly improving accuracy over text-based methods.
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10.48550/arXiv.2605.15984ToxiAlert-Bench is a comprehensive audio dataset and a dual-head neural network framework that detects toxic speech by incorporating paralinguistic cues, significantly improving accuracy over text-based methods.
Abstract
Toxic speech detection has become a crucial challenge in maintaining safe online communication environments. However, existing approaches to toxic speech detection often neglect the contribution of paralinguistic cues, such as emotion, intonation, and speech rate, which are key to detecting speech toxicity. Moreover, current toxic speech datasets are predominantly text-based, limiting the development of models that can capture paralinguistic cues.To address these challenges, we present ToxiAlert-Bench, a large-scale audio dataset comprising over 30,000 audio clips annotated with seven major toxic categories and twenty fine-grained toxic labels. Uniquely, our dataset annotates toxicity sources -- distinguishing between textual content and paralinguistic origins -- for comprehensive toxic speech analysis.Furthermore, we propose a dual-head neural network with a multi-stage training strategy tailored for toxic speech detection. This architecture features two task-specific classification headers: one for identifying the source of sensitivity (textual or paralinguistic), and the other for categorizing the specific toxic type. The training process involves independent head training followed by joint fine-tuning to reduce task interference. To mitigate data class imbalance, we incorporate class-balanced sampling and weighted loss functions.Our experimental results show that leveraging paralinguistic features significantly improves detection performance. Our method consistently outperforms existing baselines across multiple evaluation metrics, with a 21.1% relative improvement in Macro-F1 score and a 13.0% relative gain in accuracy over the strongest baseline, highlighting its enhanced effectiveness and practical applicability.
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PROBLEM
ToxiAlert-Bench is a comprehensive audio dataset and a dual-head neural network framework that detects toxic speech by incorporating paralinguistic cues, significantly improving accuracy over text-based methods. However, existing approaches to toxic speech detection often neglec...
METHOD
Toxic speech detection has become a crucial challenge in maintaining safe online communication environments. However, existing approaches to toxic speech detection often neglect the contribution of paralinguistic cues, such as emotion, intonation, and speech rate, which are key...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To mitigate data class imbalance, we incorporate class-balanced sampling and weighted loss functions.Our experimental results show that leveraging paralinguistic features significantly improves detection...
WHY NOW
Speech Toxicity Detection moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
ToxiAlert-Bench is a comprehensive audio dataset and a dual-head neural network framework that detects toxic speech by incorporating paralinguistic cues, significantly improving accuracy over text-based methods. However, existing approaches to toxic speech detection often neglect the contribution of paralinguistic cues, such as emotion, intonation, and speech rate, which are key to detecting speech toxicity.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Toxic speech detection has become a crucial challenge in maintaining safe online communication environments. However, existing approaches to toxic speech detection often neglect the contribution of paralinguistic cues, such as emotion, intonation, and speech rate, which are key to detecting speech toxicity.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To mitigate data class imbalance, we incorporate class-balanced sampling and weighted loss functions.Our experimental results show that leveraging paralinguistic features significantly improves detection performance. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Speech Toxicity Detection moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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ToxiAlert-Bench is a comprehensive audio dataset and a dual-head neural network framework that detects toxic speech by incorporating paralinguistic cues, significantly improving accuracy over text-based methods.
Segment
Speech Toxicity Detection
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