4 citations · 6 across the 3 of their papers we have counts for
4 papers
NeuFA: Neural Network Based End-to-End Forced Alignment with Bidirectional Attention Mechanism
Jingbei Li, Yi Meng, Zhiyong Wu +4
Although deep learning and end-to-end models have been widely used and shown superiority in automatic speech recognition (ASR) and text-to-speech (TTS) synthesis, state-of-the-art…
Towards Multi-Scale Style Control for Expressive Speech Synthesis
Xiang Li, Changhe Song, Jingbei Li +3
This paper introduces a multi-scale speech style modeling method for end-to-end expressive speech synthesis. The proposed method employs a multi-scale reference encoder to extract…
Syntactic representation learning for neural network based TTS with syntactic parse tree traversal
Changhe Song, Jingbei Li, Yixuan Zhou +2
Syntactic structure of a sentence text is correlated with the prosodic structure of the speech that is crucial for improving the prosody and naturalness of a text-to-speech (TTS) s…
Emotion controllable speech synthesis using emotion-unlabeled dataset with the assistance of cross-domain speech emotion recognition
Xiong Cai, Dongyang Dai, Zhiyong Wu +3
Neural text-to-speech (TTS) approaches generally require a huge number of high quality speech data, which makes it difficult to obtain such a dataset with extra emotion labels. In…