Arnaud Joly

According to our database, Arnaud Joly authored at least 19 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data.

Mateusz Lajszczak, Guillermo Cámbara, Yang Li, Fatih Beyhan, Arent van Korlaar, Fan Yang, Arnaud Joly, Álvaro Martín-Cortinas, Ammar Abbas, Adam Michalski, Alexis Moinet, Sri Karlapati, Ewa Muszynska, Haohan Guo, Bartosz Putrycz, Soledad López Gambino, Kayeon Yoo, Elena Sokolova, Thomas Drugman

CoRR, 2024

Mapache: Masked Parallel Transformer for Advanced Speech Editing and Synthesis.

Guillermo Cámbara, Patrick Lumban Tobing, Mikolaj Babianski, Ravichander Vipperla, Duo Wang, Ron Shmelkin, Giuseppe Coccia, Orazio Angelini, Arnaud Joly, Mateusz Lajszczak, Vincent Pollet

Proceedings of the IEEE International Conference on Acoustics, 2024

2023

Controllable Emphasis with zero data for text-to-speech.

Arnaud Joly, Marco Nicolis, Ekaterina Peterova, Alessandro Lombardi, Ammar Abbas, Arent van Korlaar, Aman Hussain, Parul Sharma, Alexis Moinet, Mateusz Lajszczak, Penny Karanasou, Antonio Bonafonte, Thomas Drugman, Elena Sokolova

Proceedings of the 12th ISCA Speech Synthesis Workshop, 2023

2022

Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosody.

Peter Makarov, Ammar Abbas, Mateusz Lajszczak, Arnaud Joly, Sri Karlapati, Alexis Moinet, Thomas Drugman, Penny Karanasou

CoRR, 2022

Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosody.

Peter Makarov, Syed Ammar Abbas, Mateusz Lajszczak, Arnaud Joly, Sri Karlapati, Alexis Moinet, Thomas Drugman, Penny Karanasou

Proceedings of the 23rd Annual Conference of the International Speech Communication Association, 2022

Distribution Augmentation for Low-Resource Expressive Text-To-Speech.

Mateusz Lajszczak, Animesh Prasad, Arent van Korlaar, Bajibabu Bollepalli, Antonio Bonafonte, Arnaud Joly, Marco Nicolis, Alexis Moinet, Thomas Drugman, Trevor Wood, Elena Sokolova

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

Multi-Scale Spectrogram Modelling for Neural Text-to-Speech.

Ammar Abbas, Bajibabu Bollepalli, Alexis Moinet, Arnaud Joly, Penny Karanasou, Peter Makarov, Simon Slangen, Sri Karlapati, Thomas Drugman

Proceedings of the 11th ISCA Speech Synthesis Workshop, 2021

A Learned Conditional Prior for the VAE Acoustic Space of a TTS System.

Penny Karanasou, Sri Karlapati, Alexis Moinet, Arnaud Joly, Ammar Abbas, Simon Slangen, Jaime Lorenzo-Trueba, Thomas Drugman

Proceedings of the 22nd Annual Conference of the International Speech Communication Association, Interspeech 2021, Brno, Czechia, August 30, 2021

Prosodic Representation Learning and Contextual Sampling for Neural Text-to-Speech.

Sri Karlapati, Ammar Abbas, Zack Hodari, Alexis Moinet, Arnaud Joly, Penny Karanasou, Thomas Drugman

Proceedings of the IEEE International Conference on Acoustics, 2021

Camp: A Two-Stage Approach to Modelling Prosody in Context.

Zack Hodari, Alexis Moinet, Sri Karlapati, Jaime Lorenzo-Trueba, Thomas Merritt, Arnaud Joly, Ammar Abbas, Penny Karanasou, Thomas Drugman

Proceedings of the IEEE International Conference on Acoustics, 2021

2020

CopyCat: Many-to-Many Fine-Grained Prosody Transfer for Neural Text-to-Speech.

Sri Karlapati, Alexis Moinet, Arnaud Joly, Viacheslav Klimkov, Daniel Sáez-Trigueros, Thomas Drugman

Proceedings of the 21st Annual Conference of the International Speech Communication Association, 2020

2019

Gradient tree boosting with random output projections for multi-label classification and multi-output regression.

Arnaud Joly, Louis Wehenkel, Pierre Geurts

CoRR, 2019

2017

Exploiting random projections and sparsity with random forests and gradient boosting methods - Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity.

Arnaud Joly

PhD thesis, 2017

Exploiting random projections and sparsity with random forests and gradient boosting methods - Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity.

Arnaud Joly

CoRR, 2017

Globally Induced Forest: A Prepruning Compression Scheme.

Jean-Michel Begon, Arnaud Joly, Pierre Geurts

Proceedings of the 34th International Conference on Machine Learning, 2017

2014

Random Forests with Random Projections of the Output Space for High Dimensional Multi-label Classification.

Arnaud Joly, Pierre Geurts, Louis Wehenkel

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging.

Antonio Sutera, Arnaud Joly, Vincent François-Lavet, Zixiao Aaron Qiu, Gilles Louppe, Damien Ernst, Pierre Geurts

Proceedings of the Neural Connectomics Workshop at ECML 2014, 2014

2013

API design for machine learning software: experiences from the scikit-learn project.

Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, Gaël Varoquaux

CoRR, 2013

2012

L1-based compression of random forest models.

Arnaud Joly, François Schnitzler, Pierre Geurts, Louis Wehenkel

Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012