评测语音指令系统中用户指令与听觉感知的差距，发现多数模型对情感和年龄控制不准。
Do You Hear What I Mean? Quantifying the Instruction-Perception Gap in Instruction-Guided Expressive Text-To-Speech Systems
- 通过真人评分分析指令语音在强度和情绪上的表现力控制效果。
- gpt-4o-mini-tts 最接近指令意图，但多数模型误判年龄属性。
- 细粒度控制仍不足，适合语音合成与人机交互研究者参考。
指令引导式文本转语音（ITTS）让用户通过自然语言控制语音生成，提供更直观的交互方式。然而，用户风格指令与听众感知之间的对齐程度尚未深入探究。本文首次对两种表达维度（程度副词和情绪强度分级）进行感知分析，并收集了关于说话人年龄与词级强调属性的人类评分。为全面揭示指令-感知差距，构建了大规模人类评估数据集，命名为表达性语音控制（E-VOC）语料库。结果表明：（1）gpt-4o-mini-tts 是最可靠的 ITTS 模型，在声学维度上指令与生成语音的对齐度最佳；（2）所分析的5个 ITTS 系统普遍生成成人声音，即使指令要求儿童或老年声音；（3）细粒度控制仍是主要挑战，说明多数 ITTS 系统在理解细微属性指令方面仍有巨大改进空间。
原文摘要 · Abstract (English)
Instruction-guided text-to-speech (ITTS) enables users to control speech generation through natural language prompts, offering a more intuitive interface than traditional TTS. However, the alignment between user style instructions and listener perception remains largely unexplored. This work first presents a perceptual analysis of ITTS controllability across two expressive dimensions (adverbs of degree and graded emotion intensity) and collects human ratings on speaker age and word-level emphasis attributes. To comprehensively reveal the instruction-perception gap, we provide a data collection with large-scale human evaluations, named Expressive VOice Control (E-VOC) corpus. Furthermore, we reveal that (1) gpt-4o-mini-tts is the most reliable ITTS model with great alignment between instruction and generated utterances across acoustic dimensions. (2) The 5 analyzed ITTS systems tend to generate Adult voices even when the instructions ask to use child or Elderly voices. (3) Fine-grained control remains a major challenge, indicating that most ITTS systems have substantial room for improvement in interpreting slightly different attribute instructions.
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