VocaliD SBIR Phase II: Optimized Speech Corpora for Personalized Speech Synthesis

我们的声音并不相同，它们是我们的身份。人类的声音是一种强有力的信号，它传达了一个人的 年龄、性别、体型、种族和个性，以及其他属性。然而，到目前为止，增强和 替代通信(AAC)设备、屏幕阅读技术和其他文本语音转换(TTS) 应用程序依赖于一组有限的大规模生产的、听起来通用的合成声音。中的这种不匹配 声音认同影响教育成果，侵犯人身安全，阻碍社会融合。 构建合成声音的传统方法需要配音人员录制大量的数据集 录音棚品质的录音，用于训练计算模型并生成输出语音。这个 这一过程耗费大量时间和劳动，因此日常消费者无法接触到，更不用说那些会说话的人了 减损。VocaliD Inc.的获奖技术提供了一种前所未有的方法来制造定制的 合成声音，通过将接受者自己的残存发声与 匹配我们人类语音银行的扬声器。我们发现，即使是一个元音也包含了足够的 “声音DNA”，为个性化过程播种。VocaliD的定制声音听起来像是年龄上的接受者， 个性和声音身份，但像捐赠者的录音一样清晰和易于理解。要创建一个 实惠而高效的语音个性化方法，我们利用高质量的渗透力 在消费级计算机上安装麦克风和录音软件，并提高技术素养，以 将语音和语音录音的集合进行众包。这实现了跨年龄段的参与， 在社会经济、文化和语言群体之间建立联系，以便真正了解人类声音的多样性。这个 然而，挑战在于确保高质量的录音和充分动员演讲捐赠者完成 录音语料库。这个第二阶段的项目建立在我们第一阶段的成功基础上，以缩短捐赠者的长度 语料库，并简化和自动化收件人协议。我们的感知实验结果表明 虽然我们能够将捐赠者语料库的长度减少70%，但代价是减少 可理解性和自然性。由于语音质量对于接受和采用我们的语音至关重要，因此此第二阶段 建议的目的是提高我们声音的清晰度和表现力，同时保持优化的 语料库长度。我们建议通过开发方法来缓解TTS的影响，以提高TTS的可理解性 捐赠者和接受者录音过程中的背景噪声和混响，并使预期和实际保持一致 口语记录，以减少TTS模型建立过程中的错误(目标1)。为了解决TTS的自然性问题，我们 提议修改捐助者语料库，以包括更多韵律上的不同对比，并调整捐助者议定书 引出自然的旋律语调和语法(目标2)。这些进步将产生可扩展且经济高效的 个性化语音创建方法，将使AAC及更高级别的语音支持技术人性化。

Our voices are not identical, they are our identities. The human voice is a powerful signal that conveys one's age, gender, size, ethnicity, and personality, among other attributes. Yet, until now, users of augmentative and alternative communication (AAC) devices, screen reading technologies and other text-to-speech (TTS) applications have relied on a limited set of mass-produced, generic-sounding synthetic voices. This mismatch in vocal identity impacts educational outcomes, infringes on personal safety, and hinders social integration. Conventional methods for building a synthetic voice require a voice actor to record an extensive dataset of studio-quality recordings which are used to train a computational model and generate the output voice. The process is time and labor intensive and thus inaccessible to everyday consumers let alone those with speech impairment. VocaliD Inc's award winning technology offers an unprecedented means to build custom crafted synthetic voices that reflect the recipient by combining his/her own residual vocalizations with recordings of a matched speaker from our Human Voicebank. We have discovered that even a single vowel contains enough "vocal DNA" to seed the personalization process. VocaliD's custom voice sounds like the recipient in age, personality and vocal identity but is as clear and understandable as the donor's recordings. To create an affordable and efficient method of voice personalization, we leverage the penetration of high quality microphones and recording software on consumer grade computers and increased technological literacy to crowdsource the collection of speech and voice recordings. This enables engagement across broad age, socioeconomic, cultural and linguistic groups in order to truly sample the diversity of the human voice. The challenges, however, are to ensure high quality recordings and to sufficiently engage speech donors to complete the recording corpus. This Phase II project builds upon our success in Phase I to reduce the length of the donor corpus and to streamline and automate the recipient protocol. Results of our perceptual experiments indicated that while we were able to reduce the length of the donor corpus by 70%, it came at the cost of reduced intelligibility and naturalness. Since voice quality is vital to acceptance and adoption of our voices, this Phase II proposal is aimed at improving the clarity and expressiveness of our voices while maintaining the optimized corpus length. We propose to improve TTS intelligibility by developing methods to mitigate the effects of background noise and reverberation during donor and recipient recordings and aligning expected and actual spoken transcripts to reduce errors in TTS model building (Aim 1). To address the issue of TTS naturalness, we propose to modify the donor corpus to include more prosodically diverse contrasts and adapt the donor protocol to elicit natural melodic intonation and phrasing (Aim 2). These advances will yield a scalable and cost-effective method of personalized voice creation that will humanize speech-enabled technologies for AAC and beyond.