ITR: Evaluation and Personalization of Synthetic Voices

合成语音的评价和个性化摘要本项目解决了用计算机生成自然发声的语音输出的两个关键问题。 第一方面提供了一种用于合成语音的评估策略。 由于对语音合成质量没有明确的客观衡量标准，很难显示系统和比较技术的改进。 这项工作提供了一个详细的清单，评估技术的子部分的合成过程，包括：文本分析，词汇发音，韵律，语音质量。 这些技术旨在对其他语言中现有的和新开发的声音采取行动，而这些声音通常很难获得。 适当的人体实验是用来证明给定的指标。这项工作的第二个方面是提高特定说话人的声学语音建模。 在单元选择合成技术中，将词典与特定说话者的实际口语形式相匹配可能会在选择过程中引入显著的噪声。 说话者会因他们的方言和风格而有特定的习语。 使用数据驱动的技术，在知识为基础的框架内，我们得出适当的分割类型的单元选择合成。结果通过CMU的http://festvox.org网站发布。

Evaluation and Personalization of Synthetic VoicesAbstractThis project addresses two key issues in the generation of natural sounding spoken output by computer. The first aspect provides an evaluation strategy for synthetic voices. With no clear objective measure for speech synthesis quality, it is hard to show improvement in systems and compare techniques. This work provides a detailed list of evaluation techniques for sub-parts of the synthesis process including: text analysis, lexical pronunciation, prosody, and phonetic quality. The techniques are designed to act on existing and newly developed voices in other languages, where measures are often harder to come by. Appropriate human experimentation is used to justify the given metrics.The second aspect of this work is to improve modeling of speaker-specific acoustic phonetics. In unit selection synthesis techniques, matching the lexicon with the actual spoken form of a particular speaker can introduce significant noise in the selection process. Speakers will have particular idiolects for their dialect and style. Using data-driven techniques, within knowledge-based frameworks, we derive appropriate segmentation types for unit selection synthesis.Results are released through CMU's http://festvox.org site.