Mindfulness-based therapies have been shown to be effective in improving mental health, and technology-based methods have the potential to expand the accessibility of these therapies. To enable real-time personalized content generation for mindfulness practice in these methods, high-quality computer-synthesized text-to-speech (TTS) voices are needed to provide verbal guidance and respond to user performance and preferences. However, the user-perceived quality of state-of-the-art TTS voices has not yet been evaluated for administering mindfulness meditation, which requires emotional expressiveness. In addition, work has not yet been done to study the effect of physical embodiment and personalization on the user-perceived quality of TTS voices for mindfulness. To that end, we designed a two-phase human subject study. In Phase 1, an online Mechanical Turk between-subject study (N=471) evaluated 3 (feminine, masculine, child-like) state-of-the-art TTS voices with 2 (feminine, masculine) human therapists' voices in 3 different physical embodiment settings (no agent, conversational agent, socially assistive robot) with remote participants. Building on findings from Phase 1, in Phase 2, an in-person within-subject study (N=94), we used a novel framework we developed for personalizing TTS voices based on user preferences, and evaluated user-perceived quality compared to best-rated non-personalized voices from Phase 1. We found that the best-rated human voice was perceived better than all TTS voices; the emotional expressiveness and naturalness of TTS voices were poorly rated, while users were satisfied with the clarity of TTS voices. Surprisingly, by allowing users to fine-tune TTS voice features, the user-personalized TTS voices could perform almost as well as human voices, suggesting user personalization could be a simple and very effective tool to improve user-perceived quality of TTS voice.

翻译：基于正念的疗法已被证明能有效改善心理健康，而技术手段有望扩大这些疗法的可及性。为在这些方法中实现正念练习的实时个性化内容生成，需要高质量的计算机合成语音（TTS）提供言语指导并响应用户表现与偏好。然而，当前最先进的TTS语音在需要情感表现力的正念冥想引导中的用户感知质量尚未得到评估。此外，物理具身形态与个性化对正念场景下TTS语音用户感知质量的影响也尚无研究。为此，我们设计了一个两阶段的人类受试者实验。第一阶段，在线Mechanical Turk受试者间实验（N=471）评估了三种（女性化、男性化、童声）最先进TTS语音与两种（女性化、男性化）人类治疗师语音，在三种不同物理具身形态（无智能体、对话式智能体、社交辅助机器人）下对远程参与者的效果。基于第一阶段发现，第二阶段开展线下受试者内实验（N=94），利用我们新开发的基于用户偏好的TTS语音个性化框架，评估其与第一阶段最佳非个性化语音相比的用户感知质量。研究发现：评分最高的人类语音在所有TTS语音中感知更优；TTS语音的情感表现力与自然度评分较低，但用户对其清晰度表示满意。值得注意的是，允许用户微调TTS语音特征后，个性化语音几乎能达到人类语音的表现水平，表明用户个性化可作为提升TTS语音用户感知质量的简便有效工具。