9 accepted papers
This paper integrates a classic mel-cepstral synthesis filter into a modern neural speech synthesis system towards end-to-end controllable speech synthesis. Since the mel-cepstral synthesis filter is explicitly embedded in neural waveform models in the proposed system, both voice characteristics and…
We propose PeriodNet, a non-autoregressive (non-AR) waveform generation model with a new model structure for modeling periodic and aperiodic components in speech waveforms. The non-AR waveform generation models can generate speech waveforms parallelly and can be used as a speech vocoder by condition…
The present paper describes singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized singing voices. As singing voices represent a rich for…
This paper proposes a general framework of semi-supervised learning based on hierarchical generative models and adapts it to a Japanese end-to-end text-to-speech (TTS) system. In English TTS, several end-to-end systems have recently achieved sound quality close to that of natural human speech. Howev…
This paper proposes a generative adversarial training method for deep neural network (DNN)-based singing voice synthesis. The DNN-based approach has been used in statistical parametric singing voice synthesis and improved the naturalness of the synthesized singing voice [1]. Recently, generative adv…
This paper proposes a WaveNet-based delay-free adaptive differential pulse code modulation (ADPCM) speech coding system. The WaveNet generative model, which is a state-of-the-art model for neural-network-based speech waveform synthesis, is used as the adaptive predictor in ADPCM. To further improve…
This paper proposes a voice conversion technique based on WaveNet to directly generate target audio waveforms from acoustic features of a source speaker. In voice conversion based on statistical models, the relation between acoustic features, such as spectral parameters, extracted from source and ta…
This paper proposes a new training method of deep neural networks (DNNs) for statistical parametric speech synthesis. DNNs are recently used as acoustic models that represent mapping functions from linguistic features to acoustic features in statistical parametric speech synthesis. There are problem…
This paper investigates how to use neural networks in statistical parametric speech synthesis. Recently, deep neural networks (DNNs) have been used for statistical parametric speech synthesis. However, the specific way how DNNs should be used in statistical parametric speech synthesis has not been s…