Аннотация
Speech synthesis is widely used in many practical applications. In recent years, speech synthesis technology has developed rapidly. However, one of the reasons why synthetic speech is unnatural is that it often has over-smoothness. In order to improve the naturalness of synthetic speech, we first extract the mel-spectrogram of speech and convert it into a real image, then take the over-smooth mel-spectrogram image as input, and use image-To-image translation Generative Adversarial Networks(GANs) framework to generate a more realistic mel-spectrogram. Finally, the results show that this method greatly reduces the over-smoothness of synthesized speech and is more close to the mel-spectrogram of real speech.
Язык оригинала | английский |
---|---|
Название основной публикации | 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) |
Издатель | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
Страницы | 972-974 |
Число страниц | 3 |
ISBN (электронное издание) | 978-1-7281-4401-6 |
ISBN (печатное издание) | 978-1-7281-4402-3 |
DOI | |
Состояние | Опубликовано - окт 2019 |
Ключевые слова
- Ситнез речи
- Сверх-сглаженность
- мел-спектрограммы
- Генеративные состязательные сети