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BLSTM-CRF Based End-to-End Prosodic Boundary Prediction with Context Sensitive Embeddings in a Text-to-Speech Front-End
2018
Interspeech 2018
In this paper, we propose a language-independent end-to-end architecture for prosodic boundary prediction based on BLSTM-CRF. The proposed architecture has three components, word embedding layer, BLSTM layer and CRF layer. The word embedding layer is employed to learn the task-specific embeddings for prosodic boundary prediction. The BLSTM layer can efficiently use both past and future input features, while the CRF layer can efficiently use sentence level information. We integrate these three
doi:10.21437/interspeech.2018-1472
dblp:conf/interspeech/ZhengTWL18
fatcat:r4v4bhjawnbabnxtunbpmj7bcm