Abstract
Current bias evaluations in Instruction Text-to-Speech (ITTS) often rely on univariate testing, overlooking the compositional structure of social cues. In this work, we investigate gender bias by modeling prompts as combinations of Social Status, Career stereotypes, and Persona descriptors. Analyzing open-source ITTS models, we uncover systematic interaction effects where social dimensions modulate one another, creating complex bias patterns missed by univariate baselines. Crucially, our findings indicate that these biases extend beyond surface-level artifacts, demonstrating strong associations with the semantic priors of pre-trained text encoders and the skewed distributions inherent in training data. We further demonstrate that generic diversity prompting is insufficient to override these entrenched patterns, underscoring the need for compositional analysis to diagnose latent risks in generative speech.
Abstract (translated)
当前指令文本转语音（ITTS）中的偏差评估通常依赖单变量测试，忽视了社会线索的组合结构。在本研究中，我们通过将提示词建模为社会地位、职业刻板印象与人格描述符的组合来探究性别偏差。分析开源ITTS模型后，我们发现了系统性交互效应——社会维度会相互调节，形成单变量基线方法所遗漏的复杂偏差模式。关键的是，我们的研究结果表明这些偏差不仅停留在表面特征，更与预训练文本编码器的语义先验以及训练数据中固有的分布偏差存在强关联。我们还证明，通用多样性提示不足以覆盖这些根深蒂固的模式，这凸显了采用组合分析来诊断生成式语音中潜在风险的必要性。
URL
https://arxiv.org/abs/2603.20743
