TRANSFORM: flexible voice synthesis through articulatory voice transformation

TRANSFORM：通过发音转换实现灵活的语音合成

• 批准号: 0414675
• 负责人: Alan Black
• 金额: --
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-05-15 至 2009-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0414675&HistoricalAwards=false
• 关键词: TRANSFORM; flexible; voice; synthesis; through

Many people have always wanted machines to talk to them, but most have strong preferences for particular voices. Current techniques in speech synthesis can build voices that sound very close to the original speaker, capturing the style, manner and articulation of the source voice. However such systems require many hours of carefully recorded speech and expert tuning to reach an acceptable level of quality. An exciting new alternative method for building synthetic voices is voice transformation. This method uses an existing recorded database and converts it to a target voice using as little as 10-20 sentences. This technique offers the potential to make speech synthesizers talk in whatever voice desired, with significantly less effort required than previous techniques.This project offers a new direction in voice transformation. Current transformation techniques concentrate on a spectral mapping of the voice, i.e., converting the properties of the speech signal. Instead we use the underlying positions of the vocal tract articulators (i.e., the position of the teeth, tongue, lips, velum), which give rise to the spectral output of the voice. Using new statistical modeling techniques we can successfully predict the positions of a speaker's articulators from the speech signal. Then in the virtual vocal tract domain map between speakers and regenerate the speech for the target voice.This work enables the easy construction of new synthetic voices allowing personalization of speech output. It increases our knowledge of the speech generation process and characterizes what make a voice personal.

许多人一直希望机器能与他们对话，但大多数人对特定的声音有强烈的偏好。当前的语音合成技术可以构建听起来非常接近原始说话人的声音，捕捉源声音的风格、方式和发音。然而，这种系统需要经过数小时的仔细录音和专家调整，才能达到可接受的质量水平。构建合成声音的另一种令人兴奋的新方法是声音变换。这种方法利用现有的记录数据库，只需10-20个句子就可以将其转换为目标语音。这项技术使语音合成器有可能以任何想要的声音说话，而所需的工作量比以前的技术要少得多。该项目为语音转换提供了一个新的方向。当前的变换技术集中于语音的频谱映射，即，转换语音信号的属性。相反，我们使用声道发音器的潜在位置(即牙齿、舌头、嘴唇、膜的位置)，这会引起声音的光谱输出。使用新的统计建模技术，我们可以成功地从语音信号中预测说话人发音器的位置。然后在虚拟声道域中映射说话人之间的关系，并为目标语音重新生成语音。这项工作使得能够容易地构建新的合成语音，从而允许语音输出的个性化。它增加了我们对语音生成过程的了解，并使声音具有个人化的特征。