Concept

Système d'écriture

Publications associées (33)

Task-driven neural network models predict neural dynamics of proprioception: Synthetic muscle spindle datasets

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the synthetic spindle datasets of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the synthetic generated training dataset of simulated muscle spindles during arm passive movements gene ...

Zenodo2024

Speech Style Transfer

The problem of style transfer consists in transferring the style from one signal to another while preserving the latter’s content. This project explores the applications of style transfer techniquesto speech signals. In particular, such techniques are used ...

2020

MATHICSE Technical Report : Efficient state/Parameter estimation in nonlinear unsteady PDEs by reduced basis ensemble Kalman filter

Alfio Quarteroni, Andrea Manzoni, Stefano Pagani

The ensemble Kalman filter is a computationally efficient technique to solve state and/or parameter estimation problems in the framework of statistical inversion when relying on a Bayesian paradigm. Unfortunately its cost may become moderately large for sy ...

MATHICSE2019

Character-level Chinese-English Translation through ASCII Encoding

Mi Xue Tan

Character-level Neural Machine Translation(NMT) models have recently achieved impressive results on many language pairs. They mainly do well for Indo-European language pairs, where the languages share the same writing system. However, for translating betwe ...

Association for Computational Linguistics2018

Maya Codical Glyph Segmentation: A Crowdsourcing Approach

Daniel Gatica-Perez, Jean-Marc Odobez, Gülcan Can

This paper focuses on the crowd-annotation of an ancient Maya glyph dataset derived from the three ancient codices that survived up to date. More precisely, non-expert annotators are asked to segment glyph-blocks into their constituent glyph entities. As a ...

2018

Phonetic aware techniques for Speaker Verification

Subhadeep Dey

The goal of this thesis is to improve current state-of-the-art techniques in speaker verification (SV), typically based on âidentity-vectorsâ (i-vectors) and deep neural network (DNN), by exploiting diverse (phonetic) information extracted using variou ...

EPFL2018

How to Tell Ancient Signs Apart? Recognizing and Visualizing Maya Glyphs with CNNs

Daniel Gatica-Perez, Jean-Marc Odobez, Gülcan Can

Thanks to the digital preservation of cultural heritage materials, multimedia tools (e.g., based on automatic visual processing) considerably ease the work of scholars in the humanities and help them to perform quantitative analysis of their data. In this ...

2018

Optimized scripting in Massive Open Online Courses

Frédéric Kaplan, Isabella Di Lenardo

The Time Machine MOOC, currently under preparation, is designed to provide the necessary knowledge for students to use the editing tool of the Time Machine platform. The first test case of the platform in centered on our current work on the City of Venice ...

2017

Towards Weakly Supervised Acoustic Subword Unit Discovery and Lexicon Development Using Hidden Markov Models

Ramya Rasipuram, Marzieh Razavi

Developing a phonetic lexicon for a language requires linguistic knowledge as well as human effort, which may not be available, particularly for under-resourced languages. An alternative to development of a phonetic lexicon is to automatically derive subwo ...

Idiap2017

On Modeling the Synergy Between Acoustic and Lexical Information for Pronunciation Lexicon Development

Marzieh Razavi

State-of-the-art automatic speech recognition (ASR) and text-to-speech systems require a pronunciation lexicon that maps each word to a sequence of phones. Manual development of lexicons is costly as it needs linguistic knowledge and human expertise. To fa ...

EPFL2017

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search