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cs.CL2021
Combining speakers of multiple languages to improve quality of neural voices
Javier Latorre, Charlotte Bailleul, Tuuli Morrill +2
In this work, we explore multiple architectures and training procedures for developing a multi-speaker and multi-lingual neural TTS system with the goals of a) improving the qualit…
cs.CL2021
Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems
Shubhi Tyagi, Antonio Bonafonte, Jaime Lorenzo-Trueba +1
Developing Text Normalization (TN) systems for Text-to-Speech (TTS) on new languages is hard. We propose a novel architecture to facilitate it for multiple languages while using da…
cs.CL2018
Effect of data reduction on sequence-to-sequence neural TTS
Javier Latorre, Jakub Lachowicz, Jaime Lorenzo-Trueba +4
Recent speech synthesis systems based on sampling from autoregressive neural networks models can generate speech almost undistinguishable from human recordings. However, these mode…