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.
