Antonio Bonafonte — accepted AI-conference papers

2022

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

Mateusz Lajszczak, Animesh Prasad, Arent van Korlaar, Bajibabu Bollepalli, Antonio Bonafonte, Arnaud Joly +5

ICASSP 2022accepted

This paper presents a novel data augmentation technique for text-to-speech (TTS), that allows to generate new (text, audio) training examples without requiring any additional data. Our goal is to in-crease diversity of text conditionings available during training. This helps to reduce overfitting, e…

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2021

Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems

Shubhi Tyagi, Antonio Bonafonte, Jaime Lorenzo-Trueba, Javier Latorre

NAACL 2021industry

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 data less than 3% of the size of the data used by the state of the art results on English. We treat TN as a sequence class…

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2018

Language and Noise Transfer in Speech Enhancement Generative Adversarial Network

Santiago Pascual, Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn

ICASSP 2018accepted

Speech enhancement deep learning systems usually require large amounts of training data to operate in broad conditions or real applications. This makes the adaptability of those systems into new, low resource environments an important topic. In this work, we present the results of adapting a speech…

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