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Cross-lingual Automatic Speech Recognition Exploiting Articulatory Features

Related publications (33)

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Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

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

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

EPFL Infoscience2024

Machine Learning for Modeling Stock Returns

Teng Andrea Xu

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

EPFL2024

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

Safe Deep Neural Networks

Kyle Michael Matoba

				The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is tr ...

EPFL2024

Supervised learning and inference of spiking neural networks with temporal coding

Ana Stanojevic

The way biological brains carry out advanced yet extremely energy efficient signal processing remains both fascinating and unintelligible. It is known however that at least some areas of the brain perform fast and low-cost processing relying only on a smal ...

EPFL2023

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Angelos Katharopoulos

Deep neural networks have completely revolutionized the field of machinelearning by achieving state-of-the-art results on various tasks ranging fromcomputer vision to protein folding. However, their application is hindered bytheir large computational and m ...

EPFL2022

Watermarking-based Defense against Adversarial Attacks on Deep Neural Networks

James Richard Larus, Xiaoting Li, Dinghao Wu, Junjun Zhang

The vulnerability of deep neural networks to adversarial attacks has posed significant threats to real-world applications, especially security-critical ones. Given a well-trained model, slight modifications to the input samples can cause drastic changes in ...

IEEE2021

Predicting optical transmission through complex scattering media from reflection patterns with deep neural networks

Demetri Psaltis, Eirini Kakkava

Deep neural networks (DNNs) are used to reconstruct transmission speckle intensity patterns from the respective reflection speckle intensity patterns generated by illuminated parafilm layers. The dependence of the reconstruction accuracy on the thickness o ...

2021

ON THE RELATIONSHIP BETWEEN SPEECH-BASED BREATHING SIGNAL PREDICTION EVALUATION MEASURES AND BREATHING PARAMETERS ESTIMATION

Zohreh Mostaani, Venkata Srikanth Nallanthighal

The respiratory system is one of the major components of the speech production system. Any alteration in breathing can result in changes in speech. Specific breathing characteristics, such as breathing rate and tidal volume, can indicate a person's patholo ...

2021

On The Relationship Between Speech-Based Breathing Signal Prediction Evaluation Measures And Breathing Parameters Estimation

Mathew Magimai Doss, Zohreh Mostaani, Venkata Srikanth Nallanthighal

IEEE2021