no code implementations • 23 May 2023 • Vamsikrishna Chemudupati, Marzieh Tahaei, Heitor Guimaraes, Arthur Pimentel, Anderson Avila, Mehdi Rezagholizadeh, Boxing Chen, Tiago Falk
Large self-supervised pre-trained speech models have achieved remarkable success across various speech-processing tasks.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 1 Jan 2021 • Joao Monteiro, Isabela Albuquerque, Jahangir Alam, Tiago Falk
Recent metric learning approaches parametrize semantic similarity measures through the use of an encoder trained along with a similarity model, which operates over pairs of representations.
no code implementations • LREC 2020 • Joao Monteiro, Md Jahangir Alam, Tiago Falk
Automatic speech processing applications often have to deal with the problem of aggregating local descriptors (i. e., representations of input speech data corresponding to specific portions across the time dimension) and turning them into a single fixed-dimension representation, known as global descriptor, on top of which downstream classification tasks can be performed.
1 code implementation • ICML 2020 • Joao Monteiro, Isabela Albuquerque, Jahangir Alam, R. Devon Hjelm, Tiago Falk
In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder.
1 code implementation • ICLR 2019 • Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago Falk, Ioannis Mitliagkas
Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary.