185 citations · 501 across the 25 of their papers we have counts for
8 papers · 1 filter
A Multi-Stage Multi-Codebook VQ-VAE Approach to High-Performance Neural TTS
Haohan Guo, Fenglong Xie, Frank K. Soong +2
We propose a Multi-Stage, Multi-Codebook (MSMC) approach to high-performance neural TTS synthesis. A vector-quantized, variational autoencoder (VQ-VAE) based feature analyzer is us…
ParaTTS: Learning Linguistic and Prosodic Cross-sentence Information in Paragraph-based TTS
Liumeng Xue, Frank K. Soong, Shaofei Zhang +1
Recent advancements in neural end-to-end TTS models have shown high-quality, natural synthesized speech in a conventional sentence-based TTS. However, it is still challenging to re…
Disentangling Style and Speaker Attributes for TTS Style Transfer
Xiaochun An, Frank K. Soong, Lei Xie
End-to-end neural TTS has shown improved performance in speech style transfer. However, the improvement is still limited by the available training data in both target styles and sp…
Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge
Mutian He, Jingzhou Yang, Lei He +1
End-to-end TTS requires a large amount of speech/text paired data to cover all necessary knowledge, particularly how to pronounce different words in diverse contexts, so that a neu…
Improving Performance of Seen and Unseen Speech Style Transfer in End-to-end Neural TTS
Xiaochun An, Frank K. Soong, Lei Xie
End-to-end neural TTS training has shown improved performance in speech style transfer. However, the improvement is still limited by the training data in both target styles and spe…
MBNet: MOS Prediction for Synthesized Speech with Mean-Bias Network
Yichong Leng, Xu Tan, Sheng Zhao +3
Mean opinion score (MOS) is a popular subjective metric to assess the quality of synthesized speech, and usually involves multiple human judges to evaluate each speech utterance. T…
Conversational End-to-End TTS for Voice Agent
Haohan Guo, Shaofei Zhang, Frank K. Soong +2
End-to-end neural TTS has achieved superior performance on reading style speech synthesis. However, it's still a challenge to build a high-quality conversational TTS due to the lim…
Feature reinforcement with word embedding and parsing information in neural TTS
Huaiping Ming, Lei He, Haohan Guo +1
In this paper, we propose a feature reinforcement method under the sequence-to-sequence neural text-to-speech (TTS) synthesis framework. The proposed method utilizes the multiple i…