Antonio Bonafonte

TL;DR: This work proposes the use of generative adversarial networks for speech enhancement, and operates at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them.

TL;DR: This paper introduces a first approach to emotion recognition using RAMSES, the UPC’s speech recognition system, based on standard speech recognition technology using hidden semi-continuous Markov models.

TL;DR: This article proposed an improved self-supervised method, where a single neural encoder is followed by multiple workers that jointly solve different selfsupervised tasks, and the needed consensus across different tasks naturally imposes meaningful constraints to the encoder, contributing to discover general representations and to minimize the risk of learning superficial ones.

TL;DR: Compared to standard probabilistic systems, Weighted Frequency Warping results in a significant increase in quality scores, whereas the conversion scores remain almost unaltered.

TL;DR: In this paper, a generative adversarial network (GAN) is proposed for speech enhancement, where the model is trained at the waveform level, training the model end-to-end and incorporating 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them.