I Know You're Listening: 面向人机交互（HRI）的自适应语音
I Know You're Listening: Adaptive Voice for HRI
AI总结：
本研究针对语言教学机器人专用合成语音研究不足的问题，基于微调的Matcha-TTS提出三项贡献：用表情提示生成轻量实时的表现力语音、根据环境调整音高提升适配性、增设二语清晰度模式优化元音时长以提升二语听者理解效果。
摘要：
尽管社交机器人用于语言教学的研究已被探索，但针对语言教学机器人的任务特定合成语音的研究仍然有限。鉴于语言是一项言语任务，这一缺口可能会严重影响机器人在语言教学任务中的有效性。我们通过三项贡献解决了二语（L2）教学机器人语音的缺失问题：1. 我们满足了对轻量且富有表现力的机器人语音的需求。我们使用经过微调的Matcha-TTS，通过表情符号提示生成一种富有表现力的语音，该语音可随时间呈现出一系列表达效果。这种语音能在有限的计算资源下实时运行。通过案例研究，我们发现这种语音更具表现力、更符合社交场景，且适合长时间的表达性言语场景，比如讲故事。2. 我们探索了如何让机器人语音适应物理和社交环境，以在不同场景中部署我们的语音系统。我们发现，在嘈杂且高能量的环境中提高音高和音变速率，会让机器人的语音听起来更合适，也让机器人显得更能感知当前环境。3. 我们利用二语听者难以区分的元音的已知语言学特性，构建了一个清晰度更高的面向二语听者的英语文本转语音（TTS）系统。我们采用数据驱动、基于感知的方法，研究二语使用者如何利用时长线索来解读英语中含最小对立对的紧（长）元音和松（短）元音的难词。我们发现元音时长对二语听者的感知有强烈影响，因此为Matcha-TTS创建了“二语清晰度模式”，该模式会延长紧元音的时长，同时保持松元音不变。我们发现，与基础版Matcha-TTS相比，该清晰度模式更让人感到受尊重、更易懂、更具鼓励性，同时还减少了这些有难度的紧/松元音最小对立对的转写错误。
英文摘要：
While the use of social robots for language teaching has been explored, there remains limited work on a task-specific synthesized voices for language teaching robots. Given that language is a verbal task, this gap may have severe consequences for the effectiveness of robots for language teaching tasks. We address this lack of L2 teaching robot voices through three contributions: 1. We address the need for a lightweight and expressive robot voice. Using a fine-tuned version of Matcha-TTS, we use emoji prompting to create an expressive voice that shows a range of expressivity over time. The voice can run in real time with limited compute resources. Through case studies, we found this voice more expressive, socially appropriate, and suitable for long periods of expressive speech, such as storytelling. 2. We explore how to adapt a robot's voice to physical and social ambient environments to deploy our voices in various locations. We found that increasing pitch and pitch rate in noisy and high-energy environments makes the robot's voice appear more appropriate and makes it seem more aware of its current environment. 3. We create an English TTS system with improved clarity for L2 listeners using known linguistic properties of vowels that are difficult for these listeners. We used a data-driven, perception-based approach to understand how L2 speakers use duration cues to interpret challenging words with minimal tense (long) and lax (short) vowels in English. We found that the duration of vowels strongly influences the perception for L2 listeners and created an "L2 clarity mode" for Matcha-TTS that applies a lengthening to tense vowels while leaving lax vowels unchanged. Our clarity mode was found to be more respectful, intelligible, and encouraging than base Matcha-TTS while reducing transcription errors in these challenging tense/lax minimal pairs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
