Paper Reading AI Learner

Fine-grained style modelling and transfer in text-to-speech synthesis via content-style disentanglement

2020-11-08 09:51:21
Tan Daxin, Lee Tan

Abstract

This paper presents a novel neural model for fine-grained style modeling and transfer in expressive text-to-speech (TTS) synthesis. By applying collaborative learning and adversarial learning strategies with thoughtfully designed loss functions, the proposed model is able to perform effective phoneme-level disentanglement of content factor and style factor of speech. Speech style transfer can be achieved by combining the style embedding extracted from a reference utterance with the phoneme embedding derived from the source text. Results of objective evaluation show that the synthesized speech preserves the intended content and carries similar prosody to the reference speech. Results of subjective evaluation show that the new model performs better than other fine-grained style transfer TTS models.

Abstract (translated)

URL

https://arxiv.org/abs/2011.03943

PDF

https://arxiv.org/pdf/2011.03943.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot