HCC: Medium: Synthesis and Perception of Speaker Identity

该方案解决了当只有一个小训练样本时合成说话人身份的问题。为了实现从小型训练语料库合成说话人身份的目标，该项目将解决一些问题，包括表征说话人的韵律模式的可训练抽象参数化和语音转换方法。该项目属于构建文本到语音(TTS)合成系统的一般类别，以便生成听起来像特定个人的语音(说话人身份合成，或SIS)。这类系统有许多应用，包括为预期未来成为语音生成设备(SOD)用户的神经退行性疾病患者创建个性化语音，以及消费产品和娱乐行业的许多其他应用。导航系统和移动电话等消费产品正在迅速发展，它们利用有关生成话语的语言信息。该项目还将为人类感知说话人身份提供新的工具和数据。在此过程中开发的工具和相关的知觉研究也与说话人识别系统的评估相关，该项目提供了新一代简明、可训练的说话人特征描述--S韵律模式，可以合并到这些系统中。这项拟议的研究将阐明拟议的SIS系统的权衡和算法问题，并且拟议的工作很可能在语音合成领域产生强大的智力影响。

This proposal addresses the problem of synthesizing speaker identity when only a small training sample is available. To achieve the goal of synthesis of speaker identity from a small training corpus the project will address problems including trainable abstract parameterizations of the prosodic patterns that characterize a speaker and voice conversion methods. The project falls into the general category of building Text-to-Speech (TTS) synthesis system in order to generate speech that sounds like that of a specific individual (Speaker Identity Synthesis, or SIS). Systems of this kind have numerous applications, including the creation of personalized voices for individuals with neurodegenerative disorders who anticipate becoming users of Speech Generating Devices (Sods) in the future and many other applications in the consumer products and entertainment industry. Consumer products such as navigation systems and mobile phones are rapidly being developed that make use of linguistic information about generated utterance. The project will also provide new tools and data for human perception of speaker identity. The tools developed in the process and the associated perceptual studies are also relevant for assessment of speaker recognition systems, and the project provides a new generation of concise, trainable characterizations of a speaker?s prosodic patterns that can be incorporated in these systems. The proposed study will elucidate the trade-offs and algorithm issues of the proposed SIS systems and it is likely that the proposed work will have a strong intellectual impact in the field of speech synthesis.