Large Multimodal Models (LMMs) have achieved great success recently,
demonstrating a strong capability to understand multimodal information and to
interact with human users. Despite the progress made, the challenge of
detecting high-risk interactions in multimodal settings, and in particular in
speech modality, remains largely unexplored. Conventional research on risk for
speech modality primarily emphasises the content (e.g., what is captured as
transcription). However, in speech-based interactions, paralinguistic cues in
audio can significantly alter the intended meaning behind utterances. In this
work, we propose a speech-specific risk taxonomy, covering 8 risk categories
under hostility (malicious sarcasm and threats), malicious imitation (age,
gender, ethnicity), and stereotypical biases (age, gender, ethnicity). Based on
the taxonomy, we create a small-scale dataset for evaluating current LMMs
capability in detecting these categories of risk. We observe even the latest
models remain ineffective to detect various paralinguistic-specific risks in
speech (e.g., Gemini 1.5 Pro is performing only slightly above random
baseline). Warning: this paper contains biased and offensive examples.

在多模态设置中，特别是在语音模态中，检测高风险互动的挑战仍然未被广泛探索，本文提出了一个语音特定的风险分类方法，并创建了一个小规模数据集来评估当前大型多模态模型在检测这些风险类别方面的能力。