96 citations · 272 across the 20 of their papers we have counts for
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eess.AS2020★ 3 cited
s-Transformer: Segment-Transformer for Robust Neural Speech Synthesis
Xi Wang, Huaiping Ming, Lei He +1
Neural end-to-end text-to-speech (TTS) , which adopts either a recurrent model, e.g. Tacotron, or an attention one, e.g. Transformer, to characterize a speech utterance, has achiev…
eess.AS2020★ 5 cited
Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability
Jinyu Li, Rui Zhao, Zhong Meng +8
Because of its streaming nature, recurrent neural network transducer (RNN-T) is a very promising end-to-end (E2E) model that may replace the popular hybrid model for automatic spee…
cs.SD2020★ 5 cited
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…