Zobrazeno 1 - 10
of 14 287
pro vyhledávání: '"speech detection"'
A significant challenge in automating hate speech detection on social media is distinguishing hate speech from regular and offensive language. These identify an essential category of content that web filters seek to remove. Only automated methods can
Externí odkaz:
http://arxiv.org/abs/2411.05819
This paper explores the challenges of detecting LGBTQIA+ hate speech of large language models across multiple languages, including English, Italian, Chinese and (code-switched) English-Tamil, examining the impact of machine translation and whether th
Externí odkaz:
http://arxiv.org/abs/2410.11230
Autor:
Casula, Camilla, Tonelli, Sara
Hate speech is one of the main threats posed by the widespread use of social networks, despite efforts to limit it. Although attention has been devoted to this issue, the lack of datasets and case studies centered around scarcely represented phenomen
Externí odkaz:
http://arxiv.org/abs/2410.08053
The widespread presence of hate speech on the internet, including formats such as text-based tweets and vision-language memes, poses a significant challenge to digital platform safety. Recent research has developed detection models tailored to specif
Externí odkaz:
http://arxiv.org/abs/2410.05600
Autor:
Wong, Sidney Gig-Jan
While NLP research into hate speech detection has grown exponentially in the last three decades, there has been minimal uptake or engagement from policy makers and non-profit organisations. We argue the absence of ethical frameworks have contributed
Externí odkaz:
http://arxiv.org/abs/2409.17467
Publikováno v:
CIKM 2020
Hate speech detection on online social networks has become one of the emerging hot topics in recent years. With the broad spread and fast propagation speed across online social networks, hate speech makes significant impacts on society by increasing
Externí odkaz:
http://arxiv.org/abs/2409.16673
Autor:
Pham, Lam, Lam, Phat, Nguyen, Tin, Tang, Hieu, Tran, Dat, Schindler, Alexander, Zakaryan, Taron, Polonsky, Alexander, Vu, Canh
Thanks to advancements in deep learning, speech generation systems now power a variety of real-world applications, such as text-to-speech for individuals with speech disorders, voice chatbots in call centers, cross-linguistic speech translation, etc.
Externí odkaz:
http://arxiv.org/abs/2409.15180
The surge of hate speech on social media platforms poses a significant challenge, with hate speech detection~(HSD) becoming increasingly critical. Current HSD methods focus on enriching contextual information to enhance detection performance, but the
Externí odkaz:
http://arxiv.org/abs/2409.13557
Clinical diagnosis of stuttering requires an assessment by a licensed speech-language pathologist. However, this process is time-consuming and requires clinicians with training and experience in stuttering and fluency disorders. Unfortunately, only a
Externí odkaz:
http://arxiv.org/abs/2409.10704
Recently proposed automatic pathological speech detection approaches rely on spectrogram input representations or wav2vec2 embeddings. These representations may contain pathology irrelevant uncorrelated information, such as changing phonetic content
Externí odkaz:
http://arxiv.org/abs/2409.17276