SBIR Phase I: VocaliD - Infusing Unique Vocal Identities into Synthesized Speech

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是为文本到语音应用程序创建定制的语音，使收件人能够参与对话并以自己的声音被听到。 该公司的技术融合了接受者的残余发声能力和匹配的捐赠者语音数据库，以制作个性化的声音，将接受者的声音身份与捐赠者的语音清晰度相结合。仅在美国，就有超过250万人患有语言障碍，300万至500万人患有低视力，他们依赖有限的通用机械发声声音进行辅助交流。教室里的几个孩子或工作场所的几个成年人使用相同的合成声音并不罕见。我们每个人都有一个独特的声纹，可以传达我们的年龄，性别，种族，大小和个性。到目前为止，语音合成技术的用户还没有得到这种语音的多样性和灵活性。该公司的目标是将声音的礼物送给所有需要并希望它提高他们学习，工作和娱乐方式的人。 这个小企业创新研究（SBIR）第一阶段项目旨在设计个性化的合成声音，传达收件人的独特声音身份。该公司的创新基于语音产生的源滤波器理论，该理论将语音信号分为基本独立的源组件（声带）和滤波器组件（声道的其余部分）。由于源和过滤器特性都有助于扬声器身份，关键的挑战是通过从接收者发声中提取尽可能多的身份信息并将其与来自捐赠者的语音清晰度信息相结合来创建真实的，但可以理解的语音。标准的语音转换方法需要来自捐赠者和接受者的大量语音数据以及平行语料库，这些数据对于目标应用程序是不可用的。第一阶段的工作将在设计和实现一种新型的自动语音匹配和转换过程方面取得重大进展，该过程利用健康捐赠者语音的大型数据库，从目标接受者发声的稀疏样本中生成个性化的合成语音。算法将使用定量和感知指标进行验证，以评估可理解性，相似性和自然性。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to create custom crafted voices for text-to-speech applications that empower recipients to engage in conversation and be heard in his/her own voice. The company's technology blends a recipient's residual vocal abilities and a matched donor's speech database to craft a personalized voice that combines the recipient's vocal identity with the clarity of the donor's speech. In the United States alone, there are over 2.5 million individuals with speech impairment and 3-5 million individuals with low vision who rely on a limited set of generic, mechanical sounding voices for assisted communication. It is not uncommon for several children in a classroom or adults in a workplace to use the same synthetic voice. Each one of us has a unique voiceprint that conveys our age, gender, race, size, and personality. Until now, this variety and flexibility of voice has not been afforded to users of speech synthesis technology. The company's goal is to give the gift of voice to all those who need and want it to enhance how they learn, work and play. This Small Business Innovation Research (SBIR) Phase I project aims to engineer personalized synthetic voices that convey the recipient's unique vocal identity. The company's innovation is grounded in the source-filter theory of speech production which divides the speech signal into a source component (the vocal folds) and a filter component (the rest of the vocal tract) that are largely independent. Because source and filter characteristics both contribute to speaker identity, the key challenge is to create an authentic, yet understandable voice by extracting as much identity information from recipient vocalizations as possible and combining it with speech clarity information from the donor. Standard voice conversion methods require large amounts of spoken data from donors and recipients as well as parallel corpora, which are not available for the target applications. This Phase I work will make significant advances toward the design and implementation of a novel automated voice matching and transformation process that leverages a large database of healthy donors' speech to generate personalized synthetic voices from sparse samples of target recipients' vocalizations. Algorithms will be validated using both quantitative and perceptual metrics to assess intelligibility, similarity and naturalness.