Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Salvi Giampiero"'
Autor:
La Quatra, Moreno, Turco, Maria Francesca, Svendsen, Torbjørn, Salvi, Giampiero, Orozco-Arroyave, Juan Rafael, Siniscalchi, Sabato Marco
This work is concerned with devising a robust Parkinson's (PD) disease detector from speech in real-world operating conditions using (i) foundational models, and (ii) speech enhancement (SE) methods. To this end, we first fine-tune several foundation
Externí odkaz:
http://arxiv.org/abs/2406.16128
Autor:
Salvi, Giampiero
Publikováno v:
European Student Journal of Language and Speech, 1999
This paper is concerned with automatic continuous speech recognition using trainable systems. The aim of this work is to build acoustic models for spoken Swedish. This is done employing hidden Markov models and using the SpeechDat database to train t
Externí odkaz:
http://arxiv.org/abs/2404.16547
Autor:
Salvi, Giampiero
Publikováno v:
Speech Communication Volume 48, Issue 7, July 2006, Pages 802-818
This paper describes the use of connectionist techniques in phonetic speech recognition with strong latency constraints. The constraints are imposed by the task of deriving the lip movements of a synthetic face in real time from the speech signal, by
Externí odkaz:
http://arxiv.org/abs/2401.06588
Autor:
Salvi, Giampiero
Publikováno v:
Speech Communication Volume 48, Issue 12, December 2006, Pages 1666-1676
This article investigates the possibility to use the class entropy of the output of a connectionist phoneme recogniser to predict time boundaries between phonetic classes. The rationale is that the value of the entropy should increase in proximity of
Externí odkaz:
http://arxiv.org/abs/2401.05717
We address the video prediction task by putting forth a novel model that combines (i) a novel hierarchical residual learning vector quantized variational autoencoder (HR-VQVAE), and (ii) a novel autoregressive spatiotemporal predictive model (AST-PM)
Externí odkaz:
http://arxiv.org/abs/2307.06701
We propose a multi-layer variational autoencoder method, we call HR-VQVAE, that learns hierarchical discrete representations of the data. By utilizing a novel objective function, each layer in HR-VQVAE learns a discrete representation of the residual
Externí odkaz:
http://arxiv.org/abs/2208.04554
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, Volume: 45 Issue: 4, Page(s): 4997-5009
The goal of the Acoustic Question Answering (AQA) task is to answer a free-form text question about the content of an acoustic scene. It was inspired by the Visual Question Answering (VQA) task. In this paper, based on the previously introduced CLEAR
Externí odkaz:
http://arxiv.org/abs/2106.06147
In this study, we introduce a novel unsupervised countermeasure for smart grid power systems, based on generative adversarial networks (GANs). Given the pivotal role of smart grid systems (SGSs) in urban life, their security is of particular importan
Externí odkaz:
http://arxiv.org/abs/2009.05184
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2009, Iss 1, p 191940 (2009)
This paper describes SynFace, a supportive technology that aims at enhancing audio-based spoken communication in adverse acoustic conditions by providing the missing visual information in the form of an animated talking head. Firstly, we describe the
Externí odkaz:
https://doaj.org/article/65e2da0a000c4f588592b7eef73b4d82
