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Regression Networks for Meta-Learning Few-Shot Classification

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Template-matching for text-dependent speaker verification

Petr Motlicek, Subhadeep Dey

In the last decade, i-vector and Joint Factor Analysis (JFA) approaches to speaker modeling have become ubiquitous in the area of automatic speaker recognition. Both of these techniques involve the computation of posterior probabilities, using either Gauss ...

2017

Low-Rank Representation For Enhanced Deep Neural Network Acoustic Models

Automatic speech recognition (ASR) is a fascinating area of research towards realizing humanmachine interactions. After more than 30 years of exploitation of Gaussian Mixture Models (GMMs), state-of-the-art systems currently rely on Deep Neural Network (DN ...

Idiap2016

Redundant Hash Addressing for Large-Scale Query by Example Spoken Query Detection

Hervé Bourlard, Afsaneh Asaei, Dhananjay Ram

State of the art query by example spoken term detection (QbE-STD) systems rely on representation of speech in terms of sequences of class-conditional posterior probabilities estimated by deep neural network (DNN). The posteriors are often used for pattern ...

Idiap2016

Phonetic and Phonological Posterior Search Space Hashing Exploiting Class-Specific Sparsity Structures

Hervé Bourlard, Milos Cernak, Afsaneh Asaei

This paper shows that exemplar-based speech processing using class-conditional posterior probabilities admits a highly effective search strategy relying on posteriors' intrinsic sparsity structures. The posterior probabilities are estimated for phonetic an ...

Idiap2016

Evaluation of control strategies for fixed-wing drones following slow-moving ground agents

Dario Floreano, Jean-Christophe Zufferey, Grégoire Hilaire Marie Heitz, Maja Varga

There are many situations where fixed-wing drones may be required to track ground moving agents, such as humans or cars, which are typically slower than drones. Some control strategies have been proposed and validated in simulations using the average dista ...

Elsevier2015

Principal Polynomial Analysis

Devis Tuia

This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of ma ...

World Scientific Publ Co Pte Ltd2014

Tractable Approaches to Learning and Planning in High Dimensions

Leonidas Lefakis

The continuous increase, witnessed in the last decade, of both the amount of available data and the areas of application of machine learning, has lead to a demand for both learning and planning algorithms that are capable of handling large-scale problems. ...

EPFL2014

Object Classification and Detection in High Dimensional Feature Space

Charles Dubout

Object classification and detection aim at recognizing and localizing objects in real-world images. They are fundamental computer vision problems and a prerequisite for full scene understanding. Their difficulty lies in the large number of possible object ...

Programme doctoral en Informatique, Communications et Information2013

Improved Phone Posterior Estimation Through k-NN and MLP-Based Similarity

Benjamin Picart

In this work, we investigate the possible use of k-nearest neighbour (kNN) classifiers to perform frame-based acoustic phonetic classification, hence replacing Gaussian Mixture Models (GMM) or MultiLayer Perceptrons (MLP) used in standard Hidden Markov Mod ...

Idiap2009

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000