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Adaptability and controllability are the major advantages of statistical parametric speech synthesis (SPSS) over unit-selection synthesis. Recently, deep neural networks (DNNs) have significantly improved the performance of SPSS. However, current studies are mainly focusing on the training of speaker-dependent DNNs, which generally requires a significant amount of data from a single speaker. In this...
State-of-the-art statistical parametric speech synthesis (SPSS) generally uses a vocoder to represent speech signals and parameterize them into features for subsequent modeling. Magnitude spectrum has been a dominant feature over the years. Although perceptual studies have shown that phase spectrum is essential to the quality of synthesized speech, it is often ignored by using a minimum phase filter...
Prosody affects the naturalness and intelligibility of speech. However, automatic prosody prediction from text for Chinese speech synthesis is still a great challenge and the traditional conditional random fields (CRF) based method always heavily relies on feature engineering. In this paper, we propose to use neural networks to predict prosodic boundary labels directly from Chinese characters without...
In this demonstration, we introduce our recent progress on speech and auditory technologies for potential ubiquitous, immersive and personalized applications. The first demo shows an intelligent spoken question answering system, which enables users to interact with a talking avatar via natural speech dialogues. The prototype system demonstrates our latest development on automatic speech recognition,...
This paper presents a very low bit rate speech-to-video synthesizer, named lip assistant, to help hearing impaired people to better access multimedia services via lipreading. Lip assistant can automatically convert acoustic speech to lip parameters with a bit rate of 2.2 kbps, and decode them to video-realistic mouth animation on the fly. We use multi-stream HMMs (MSHMMs) and the principal component...
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