469 citations · 808 across the 29 of their papers we have counts for
9 papers · 1 filter
Better audio representations are more brain-like: linking model-brain alignment with performance in downstream auditory tasks
Leonardo Pepino, Pablo Riera, Juan Kamienkowski +1
Artificial neural networks are increasingly powerful models of brain computation, yet it remains unclear whether improving their performance in downstream tasks also makes their in…
Good practices for evaluation of machine learning systems
Luciana Ferrer, Odette Scharenborg, Tom Bäckström
Many development decisions affect the results obtained from ML experiments: training data, features, model architecture, hyperparameters, test data, etc. Among these aspects, argua…
Fusion approaches for emotion recognition from speech using acoustic and text-based features
Leonardo Pepino, Pablo Riera, Luciana Ferrer +1
In this paper, we study different approaches for classifying emotions from speech using acoustic and text-based features. We propose to obtain contextualized word embeddings with B…
On the Stability of a non-hyperbolic nonlinear map with non-bounded set of non-isolated fixed points with applications to Machine Learning
Roberta Hansen, Matias Vera, Lautaro Estienne +2
This paper deals with the convergence analysis of the SUCPA (Semi Unsupervised Calibration through Prior Adaptation) algorithm, defined from a first-order non-linear difference equ…
Analysis and Comparison of Classification Metrics
Luciana Ferrer
A variety of different performance metrics are commonly used in the machine learning literature for the evaluation of classification systems. Some of the most common ones for measu…
A discriminative condition-aware backend for speaker verification
Luciana Ferrer, Mitchell McLaren
We present a scoring approach for speaker verification that mimics the standard PLDA-based backend process used in most current speaker verification systems. However, unlike the st…
Joint PLDA for Simultaneous Modeling of Two Factors
Luciana Ferrer, Mitchell McLaren
Probabilistic linear discriminant analysis (PLDA) is a method used for biometric problems like speaker or face recognition that models the variability of the samples using two late…
Scoring Formulation for Multi-Condition Joint PLDA
Luciana Ferrer
The joint PLDA model, is a generalization of PLDA where the nuisance variable is no longer considered independent across samples, but potentially shared (tied) across samples that…
Joint Probabilistic Linear Discriminant Analysis
Luciana Ferrer
Standard probabilistic linear discriminant analysis (PLDA) for speaker recognition assumes that the sample's features (usually, i-vectors) are given by a sum of three terms: a term…