R
Roberto Barra-Chicote
Researcher at Amazon.com
Publications - 66
Citations - 984
Roberto Barra-Chicote is an academic researcher from Amazon.com. The author has contributed to research in topics: Speech synthesis & Computer science. The author has an hindex of 17, co-authored 58 publications receiving 707 citations. Previous affiliations of Roberto Barra-Chicote include Technical University of Madrid.
Papers
More filters
Journal ArticleDOI
Feature extraction from smartphone inertial signals for human activity segmentation
Rubén San-Segundo,Juan Manuel Montero,Roberto Barra-Chicote,Fernando Fernández,José Manuel Pardo +4 more
TL;DR: Adapted MFCC and PLP coefficients improve human activity recognition and segmentation accuracies while reducing feature vector size considerably, overcome significantly baseline error rates and contribute significantly to reduce the segmentation error rate.
Proceedings ArticleDOI
Towards achieving robust universal neural vocoding
Jaime Lorenzo-Trueba,Thomas Drugman,Javier Latorre,Thomas Merritt,Bartosz Putrycz,Roberto Barra-Chicote,Alexis Moinet,Vatsal Aggarwal +7 more
TL;DR: A WaveRNN-based vocoder is shown to be capable of generating speech of consistently good quality 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.
Journal ArticleDOI
Analysis of statistical parametric and unit selection speech synthesis systems applied to emotional speech
Roberto Barra-Chicote,Junichi Yamagishi,Simon King,Juan Manuel Montero,Javier Macias-Guarasa +4 more
TL;DR: The analysis shows that, although the HMM method produces significantly better neutral speech, the two methods produce emotional speech of similar quality, except for emotions having context-dependent prosodic patterns.
Proceedings ArticleDOI
BOFFIN TTS: Few-Shot Speaker Adaptation by Bayesian Optimization
TL;DR: Results indicate, across multiple corpora, that BOFFIN TTS can learn to synthesize new speakers using less than ten minutes of audio, achieving the same naturalness as produced for the speakers used to train the base model.
Posted Content
Towards achieving robust universal neural vocoding
Jaime Lorenzo-Trueba,Thomas Drugman,Javier Latorre,Thomas Merritt,Bartosz Putrycz,Roberto Barra-Chicote,Alexis Moinet,Vatsal Aggarwal +7 more
TL;DR: The authors trained a WaveRNN-based vocoder on 74 speakers coming from 17 languages and found that the results were consistent across languages, regardless of them being seen during training or unseen (e.g. Wolof, Swahili, Ahmaric).