6 citations · 10 across the 8 of their papers we have counts for
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An Efficient End-to-End Approach to Noise Invariant Speech Features via Multi-Task Learning
Heitor R. Guimarães, Arthur Pimentel, Anderson R. Avila +3
Self-supervised speech representation learning enables the extraction of meaningful features from raw waveforms. These features can then be efficiently used across multiple downstr…
On the Impact of Quantization and Pruning of Self-Supervised Speech Models for Downstream Speech Recognition Tasks "In-the-Wild''
Arthur Pimentel, Heitor Guimarães, Anderson R. Avila +2
Recent advances with self-supervised learning have allowed speech recognition systems to achieve state-of-the-art (SOTA) word error rates (WER) while requiring only a fraction of t…
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks
Heitor R. Guimarães, Arthur Pimentel, Anderson Avila +1
Keyword spotting (KWS) refers to the task of identifying a set of predefined words in audio streams. With the advances seen recently with deep neural networks, it has become a popu…
On the Transferability of Whisper-based Representations for "In-the-Wild" Cross-Task Downstream Speech Applications
Vamsikrishna Chemudupati, Marzieh Tahaei, Heitor Guimaraes +5
Large self-supervised pre-trained speech models have achieved remarkable success across various speech-processing tasks. The self-supervised training of these models leads to unive…
An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for "In the Wild'' Edge Applications
Heitor Guimarães, Arthur Pimentel, Anderson Avila +2
Unsupervised speech models are becoming ubiquitous in the speech and machine learning communities. Upstream models are responsible for learning meaningful representations from raw…
RobustDistiller: Compressing Universal Speech Representations for Enhanced Environment Robustness
Heitor R. Guimarães, Arthur Pimentel, Anderson R. Avila +3
Self-supervised speech pre-training enables deep neural network models to capture meaningful and disentangled factors from raw waveform signals. The learned universal speech repres…