  This Ph.D. thesis focuses on developing a system for high-quality speech
synthesis and voice conversion. Vocoder-based speech analysis, manipulation,
and synthesis plays a crucial role in various kinds of statistical parametric
speech research. Although there are vocoding methods which yield close to
natural synthesized speech, they are typically computationally expensive, and
are thus not suitable for real-time implementation, especially in embedded
environments. Therefore, there is a need for simple and computationally
feasible digital signal processing algorithms for generating high-quality and
natural-sounding synthesized speech. In this dissertation, I propose a solution
to extract optimal acoustic features and a new waveform generator to achieve
higher sound quality and conversion accuracy by applying advances in deep
learning. The approach remains computationally efficient. This challenge
resulted in five thesis groups, which are briefly summarized below.
