In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …

Nowadays speech synthesis or text to speech (TTS), an ability of system to produce human
like natural sounding voice from the written text, is gaining popularity in the field of speech …

We propose a postfilter based on a generative adversarial network (GAN) to compensate for
the differences between natural speech and speech synthesized by statistical parametric …

Quality of text-to-speech voices built from noisy recordings is diminished. In order to improve
it we propose the use of a recurrent neural network to enhance acoustic parameters prior to …

As an indispensable part of modern human-computer interaction system, speech synthesis
technology helps users get the output of intelligent machine more easily and intuitively, thus …

Neural sequence-to-sequence text-to-speech synthesis (TTS) can produce high-quality
speech directly from text or simple linguistic features such as phonemes. Unlike traditional …

We propose two novel techniques-stacking bottleneck features and minimum generation
error (MGE) training criterion-to improve the performance of deep neural network (DNN) …

Background Cleft palate patients have inability to produce adequate velopharyngeal
closure, which results in hypernasal speech. In clinic, hypernasal speech is assessed …

Owing to the success of deep learning techniques in automatic speech recognition, deep
neural networks (DNNs) have been used as acoustic models for statistical parametric …

A deep neural network approach to voice conversion usually depends on a large amount of
parallel training data from source and target speakers. In this paper, we propose a novel …