Abstract
This paper proposes an automatic speech recognition (ASR) model for hate speech using large language models (LLMs). The proposed method integrates the encoder of the ASR model with the decoder of the LLMs, enabling simultaneous transcription and censorship tasks to prevent the exposure of harmful content. Instruction tuning of the LLM to mask hate-related words with specific tokens requires an annotated hate speech dataset, which is limited. We generate text samples using an LLM with the Chain-of-Thought (CoT) prompting technique guided by cultural context and examples and then convert them into speech samples using a text-to-speech (TTS) system. However, some of them contain non-hate speech samples with hate-related words, which degrades the censorship performance. This paper filters the samples which text classification models correctly label as hate content. By adjusting the threshold for the number of correct answer models, we can control the level of hate in the generated dataset, allowing us to train the LLMs through curriculum learning in a gradual manner. Experimental results show that the proposed method achieves a masking accuracy of 58.6\% for hate-related words, surpassing previous baselines. We also confirm that the curriculum training contributes to the efficiency of both transcription and censorship tasks.
Abstract (translated)
本文提出了一种使用大型语言模型（LLM）进行仇恨言论自动语音识别（ASR）的模型。该方法将ASR模型的编码器与LLMs的解码器相结合，使得转录和审查任务能够同时执行，以防止有害内容的传播。为了通过特定标记屏蔽与仇恨相关的词汇对LLMs进行指令微调，需要一个带有标注的仇恨言论数据集，但这样的数据集较为有限。我们使用了大型语言模型，并结合了“思维链”（CoT）提示技术，在文化背景和示例引导下生成文本样本，然后通过文字转语音（TTS）系统将其转换为语音样本。然而，部分生成的样本中包含含有仇恨相关词汇但不属于仇恨言论的非仇恨言论样本，这降低了审查性能。本文的方法筛选出被文本分类模型正确标注为仇恨内容的样本。通过调整判定为正确答案的模型数量阈值，我们可以控制生成数据集中的仇恨程度，从而允许我们逐步进行课程学习训练LLMs。实验结果显示，该方法在与仇恨相关的词汇屏蔽准确率上达到了58.6%，超过了之前的基准水平。此外，我们也确认了课程训练对于转录和审查任务的有效性有所提升。
URL
https://arxiv.org/abs/2601.04654
