Search Results for author: Javier Latorre

Found 6 papers, 3 papers with code

Improving the quality of neural TTS using long-form content and multi-speaker multi-style modeling

no code implementations20 Dec 2022 Tuomo Raitio, Javier Latorre, Andrea Davis, Tuuli Morrill, Ladan Golipour

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.

Combining speakers of multiple languages to improve quality of neural voices

no code implementations17 Aug 2021 Javier Latorre, Charlotte Bailleul, Tuuli Morrill, Alistair Conkie, Yannis Stylianou

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 quality when the available data in the target language is limited and b) enabling cross-lingual synthesis.

Towards achieving robust universal neural vocoding

1 code implementation4 Jul 2019 Jaime Lorenzo-Trueba, Thomas Drugman, Javier Latorre, Thomas Merritt, Bartosz Putrycz, Roberto Barra-Chicote, Alexis Moinet, Vatsal Aggarwal

This vocoder is shown to be capable of generating speech of consistently good quality (98% relative mean MUSHRA when compared to natural speech) regardless of whether the input spectrogram comes from a speaker or style seen during training or from an out-of-domain scenario when the recording conditions are studio-quality.

Effect of data reduction on sequence-to-sequence neural TTS

no code implementations15 Nov 2018 Javier Latorre, Jakub Lachowicz, Jaime Lorenzo-Trueba, Thomas Merritt, Thomas Drugman, Srikanth Ronanki, Klimkov Viacheslav

Recent speech synthesis systems based on sampling from autoregressive neural networks models can generate speech almost undistinguishable from human recordings.

Speech Synthesis

Robust universal neural vocoding

8 code implementations15 Nov 2018 Jaime Lorenzo-Trueba, Thomas Drugman, Javier Latorre, Thomas Merritt, Bartosz Putrycz, Roberto Barra-Chicote

This paper introduces a robust universal neural vocoder trained with 74 speakers (comprised of both genders) coming from 17 languages.

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