ViToSA: Audio-Based Toxic Spans Detection on Vietnamese Speech Utterances

• URL: http://arxiv.org/abs/2506.00636v1
• Date: Sat, 31 May 2025 17:01:18 GMT
• Title: ViToSA: Audio-Based Toxic Spans Detection on Vietnamese Speech Utterances
• Authors: Huy Ba Do, Vy Le-Phuong Huynh, Luan Thanh Nguyen,
• Abstract summary: This paper introduces ViToSA (Vietnamese Toxic Spans Audio), the first dataset for toxic spans detection in Vietnamese speech.<n>We propose a pipeline that combines ASR and toxic spans detection for fine-grained identification of toxic content.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Toxic speech on online platforms is a growing concern, impacting user experience and online safety. While text-based toxicity detection is well-studied, audio-based approaches remain underexplored, especially for low-resource languages like Vietnamese. This paper introduces ViToSA (Vietnamese Toxic Spans Audio), the first dataset for toxic spans detection in Vietnamese speech, comprising 11,000 audio samples (25 hours) with accurate human-annotated transcripts. We propose a pipeline that combines ASR and toxic spans detection for fine-grained identification of toxic content. Our experiments show that fine-tuning ASR models on ViToSA significantly reduces WER when transcribing toxic speech, while the text-based toxic spans detection (TSD) models outperform existing baselines. These findings establish a novel benchmark for Vietnamese audio-based toxic spans detection, paving the way for future research in speech content moderation.

Related papers

• ToxicTone: A Mandarin Audio Dataset Annotated for Toxicity and Toxic Utterance Tonality [35.517662288248225]
  ToxicTone is the largest public dataset of its kind.<n>Our data is sourced from diverse real-world audio and organized into 13 topical categories.<n>We propose a multimodal detection framework that integrates acoustic, linguistic, and emotional features.
  arXiv  Detail & Related papers  (2025-05-21T17:25:27Z)
• Where are we in audio deepfake detection? A systematic analysis over generative and detection models [59.09338266364506]
  SONAR is a synthetic AI-Audio Detection Framework and Benchmark.<n>It provides a comprehensive evaluation for distinguishing cutting-edge AI-synthesized auditory content.<n>It is the first framework to uniformly benchmark AI-audio detection across both traditional and foundation model-based detection systems.
  arXiv  Detail & Related papers  (2024-10-06T01:03:42Z)
• An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarios [76.11409260727459]
  This paper explores the language adaptation capability of ZMM-TTS, a recent SSL-based multilingual TTS system. We demonstrate that the similarity in phonetics between the pre-training and target languages, as well as the language category, affects the target language's adaptation performance.
  arXiv  Detail & Related papers  (2024-06-13T08:16:52Z)
• Unveiling the Implicit Toxicity in Large Language Models [77.90933074675543]
  The open-endedness of large language models (LLMs) combined with their impressive capabilities may lead to new safety issues when being exploited for malicious use. We show that LLMs can generate diverse implicit toxic outputs that are exceptionally difficult to detect via simply zero-shot prompting. We propose a reinforcement learning (RL) based attacking method to further induce the implicit toxicity in LLMs.
  arXiv  Detail & Related papers  (2023-11-29T06:42:36Z)
• Comprehensive Assessment of Toxicity in ChatGPT [49.71090497696024]
  We evaluate the toxicity in ChatGPT by utilizing instruction-tuning datasets. prompts in creative writing tasks can be 2x more likely to elicit toxic responses. Certain deliberately toxic prompts, designed in earlier studies, no longer yield harmful responses.
  arXiv  Detail & Related papers  (2023-11-03T14:37:53Z)
• ADIMA: Abuse Detection In Multilingual Audio [28.64185949388967]
  Abusive content detection in spoken text can be addressed by performing Automatic Speech Recognition (ASR) and leveraging advancements in natural language processing. We propose ADIMA, a novel, linguistically diverse, ethically sourced, expert annotated and well-balanced multilingual profanity detection audio dataset.
  arXiv  Detail & Related papers  (2022-02-16T11:09:50Z)
• COLD: A Benchmark for Chinese Offensive Language Detection [54.60909500459201]
  We use COLDataset, a Chinese offensive language dataset with 37k annotated sentences. We also propose textscCOLDetector to study output offensiveness of popular Chinese language models. Our resources and analyses are intended to help detoxify the Chinese online communities and evaluate the safety performance of generative language models.
  arXiv  Detail & Related papers  (2022-01-16T11:47:23Z)
• Speech Toxicity Analysis: A New Spoken Language Processing Task [32.297717021285344]
  Toxic speech, also known as hate speech, is regarded as one of the crucial issues plaguing online social media today. We propose a new Spoken Language Processing task of detecting toxicity from spoken speech. We introduce DeToxy, the first publicly available toxicity annotated dataset for English speech, sourced from various openly available speech databases.
  arXiv  Detail & Related papers  (2021-10-14T17:51:04Z)
• WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans [2.4737119633827174]
  In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms. Social media platforms have worked on developing automatic detection methods and employing human moderators to cope with this deluge of offensive content.
  arXiv  Detail & Related papers  (2021-04-09T22:52:26Z)
• Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese [0.32228025627337864]
  In this paper, we create a dataset for classifying constructive and toxic speech detection with 10,000 human-annotated comments. We propose a system for constructive and toxic speech detection with the state-of-the-art transfer learning model in Vietnamese NLP as PhoBERT. With the results, we can solve some problems on the online discussions and develop the framework for identifying constructiveness and toxicity Vietnamese social media comments automatically.
  arXiv  Detail & Related papers  (2021-03-18T08:04:12Z)
• RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models [93.151822563361]
  Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise toxic language which hinders their safe deployment. We investigate the extent to which pretrained LMs can be prompted to generate toxic language, and the effectiveness of controllable text generation algorithms at preventing such toxic degeneration.
  arXiv  Detail & Related papers  (2020-09-24T03:17:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.