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.

基于人工合成的文本转语音技术 (TTS) 的用户个性化能显著改善用户心理健康感知质量，为正念实践提供实时个性化内容生成。

评估和个性化用户感知的用于传递不同物理体现的正念冥想的文本转语音音色质量

Evaluating and Personalizing User-Perceived Quality of Text-to-Speech  Voices for Delivering Mindfulness Meditation with Different Physical  Embodiments

It is time-consuming to find the best product among many similar
alternatives. Comparative sentences can help to contrast one item from others
in a way that highlights important features of an item that stand out. Given
reviews of one or multiple items and relevant item features, we generate
comparative review sentences to aid users to find the best fit. Specifically,
our model consists of three successive components in a transformer: (i) an item
encoding module to encode an item for comparison, (ii) a comparison generation
module that generates comparative sentences in an autoregressive manner, (iii)
a novel decoding method for user personalization. We show that our pipeline
generates fluent and diverse comparative sentences. We run experiments on the
relevance and fidelity of our generated sentences in a human evaluation study
and find that our algorithm creates comparative review sentences that are
relevant and truthful.

通过对比评价句子帮助用户找到最合适的产品，我们的模型由三个部分组成：对比模块、生成模块和个性化解码方法，实验证实了我们的算法生成的对比评价句子相关且真实。