57 citations · 74 across the 10 of their papers we have counts for
11 papers
Unsupervised Approaches for Global Prosodic Embedding Extraction
Martin Meza, Luciana Ferrer, Pablo Riera
Prosody is central to oral communication, conveying information like the emotional state of the speaker and cues needed for meaning disambiguation. Many self-supervised models of s…
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…
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…
EnCodecMAE: Leveraging neural codecs for universal audio representation learning
Leonardo Pepino, Pablo Riera, Luciana Ferrer
The goal of universal audio representation learning is to obtain foundational models that can be used for a variety of downstream tasks involving speech, music and environmental so…
Mispronunciation detection using self-supervised speech representations
Jazmin Vidal, Pablo Riera, Luciana Ferrer
In recent years, self-supervised learning (SSL) models have produced promising results in a variety of speech-processing tasks, especially in contexts of data scarcity. In this pap…
Phone and speaker spatial organization in self-supervised speech representations
Pablo Riera, Manuela Cerdeiro, Leonardo Pepino +1
Self-supervised representations of speech are currently being widely used for a large number of applications. Recently, some efforts have been made in trying to analyze the type of…