2 citations · 2 across the 3 of their papers we have counts for
9 papers
Distribution augmentation for low-resource expressive text-to-speech
Mateusz Lajszczak, Animesh Prasad, Arent van Korlaar +8
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.…
Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems
Shubhi Tyagi, Antonio Bonafonte, Jaime Lorenzo-Trueba +1
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 da…
Prosodic Phrase Alignment for Machine Dubbing
Alp Öktem, Mireia Farrús, Antonio Bonafonte
Dubbing is a type of audiovisual translation where dialogues are translated and enacted so that they give the impression that the media is in the target language. It requires a car…
Problem-Agnostic Speech Embeddings for Multi-Speaker Text-to-Speech with SampleRNN
David Álvarez, Santiago Pascual, Antonio Bonafonte
Text-to-speech (TTS) acoustic models map linguistic features into an acoustic representation out of which an audible waveform is generated. The latest and most natural TTS systems…
Towards Generalized Speech Enhancement with Generative Adversarial Networks
Santiago Pascual, Joan Serrà, Antonio Bonafonte
The speech enhancement task usually consists of removing additive noise or reverberation that partially mask spoken utterances, affecting their intelligibility. However, little att…
Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks
Santiago Pascual, Mirco Ravanelli, Joan Serrà +2
Learning good representations without supervision is still an open issue in machine learning, and is particularly challenging for speech signals, which are often characterized by l…