Search Results for author: Thomas Merritt

Found 13 papers, 3 papers with code

AE-Flow: AutoEncoder Normalizing Flow

no code implementations27 Dec 2023 Jakub Mosiński, Piotr Biliński, Thomas Merritt, Abdelhamid Ezzerg, Daniel Korzekwa

The results show that the proposed training paradigm systematically improves speaker similarity and naturalness when compared to regular training methods of normalizing flows.

Voice Conversion

Creating New Voices using Normalizing Flows

no code implementations22 Dec 2023 Piotr Bilinski, Thomas Merritt, Abdelhamid Ezzerg, Kamil Pokora, Sebastian Cygert, Kayoko Yanagisawa, Roberto Barra-Chicote, Daniel Korzekwa

As there is growing interest in synthesizing voices of new speakers, here we investigate the ability of normalizing flows in text-to-speech (TTS) and voice conversion (VC) modes to extrapolate from speakers observed during training to create unseen speaker identities.

Speech Synthesis Voice Conversion

Remap, warp and attend: Non-parallel many-to-many accent conversion with Normalizing Flows

no code implementations10 Nov 2022 Abdelhamid Ezzerg, Thomas Merritt, Kayoko Yanagisawa, Piotr Bilinski, Magdalena Proszewska, Kamil Pokora, Renard Korzeniowski, Roberto Barra-Chicote, Daniel Korzekwa

Regional accents of the same language affect not only how words are pronounced (i. e., phonetic content), but also impact prosodic aspects of speech such as speaking rate and intonation.

GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversion

no code implementations4 Jul 2022 Magdalena Proszewska, Grzegorz Beringer, Daniel Sáez-Trigueros, Thomas Merritt, Abdelhamid Ezzerg, Roberto Barra-Chicote

We evaluate our models in terms of intelligibility, speaker similarity and naturalness for intra- and cross-lingual conversion in seen and unseen languages.

Voice Conversion

Expressive, Variable, and Controllable Duration Modelling in TTS

no code implementations28 Jun 2022 Ammar Abbas, Thomas Merritt, Alexis Moinet, Sri Karlapati, Ewa Muszynska, Simon Slangen, Elia Gatti, Thomas Drugman

First, we propose a duration model conditioned on phrasing that improves the predicted durations and provides better modelling of pauses.

Normalising Flows Speech Synthesis

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

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.

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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