8 citations · 8 across the 5 of their papers we have counts for
6 papers
Positional Description for Numerical Normalization
Deepanshu Gupta, Javier Latorre
We present a Positional Description Scheme (PDS) tailored for digit sequences, integrating placeholder value information for each digit. Given the structural limitations of subword…
Improving the quality of neural TTS using long-form content and multi-speaker multi-style modeling
Tuomo Raitio, Javier Latorre, Andrea Davis +2
Neural text-to-speech (TTS) can provide quality close to natural speech if an adequate amount of high-quality speech material is available for training. However, acquiring speech d…
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…
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…
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…
Towards achieving robust universal neural vocoding
Jaime Lorenzo-Trueba, Thomas Drugman, Javier Latorre +5
This paper explores the potential universality of neural vocoders. We train a WaveRNN-based vocoder on 74 speakers coming from 17 languages. This vocoder is shown to be capable of…