Search Results for author: Thomas Merritt

Found 6 papers, 3 papers with code

Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech

no code implementations24 Jun 2021 Raahil Shah, Kamil Pokora, Abdelhamid Ezzerg, Viacheslav Klimkov, Goeric Huybrechts, Bartosz Putrycz, Daniel Korzekwa, Thomas Merritt

In this paper, we present a method for building highly expressive TTS voices with as little as 15 minutes of speech data from the target speaker.

Low-resource expressive text-to-speech using data augmentation

no code implementations11 Nov 2020 Goeric Huybrechts, Thomas Merritt, Giulia Comini, Bartek Perz, Raahil Shah, Jaime Lorenzo-Trueba

While recent neural text-to-speech (TTS) systems perform remarkably well, they typically require a substantial amount of recordings from the target speaker reading in the desired speaking style.

Data Augmentation Voice Conversion

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.

Robust universal neural vocoding

7 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.

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

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