57 citations · 75 across the 12 of their papers we have counts for
13 papers
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…
Benchmarking Time-localized Explanations for Audio Classification Models
Cecilia Bolaños, Leonardo Pepino, Martin Meza +1
Most modern approaches for audio processing are opaque, in the sense that they do not provide an explanation for their decisions. For this reason, various methods have been propose…
A Dataset for Automatic Assessment of TTS Quality in Spanish
Alejandro Sosa Welford, Leonardo Pepino
This work addresses the development of a database for the automatic assessment of text-to-speech (TTS) systems in Spanish, aiming to improve the accuracy of naturalness prediction…
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…
Leveraging Pre-Trained Autoencoders for Interpretable Prototype Learning of Music Audio
Pablo Alonso-Jiménez, Leonardo Pepino, Roser Batlle-Roca +4
We present PECMAE, an interpretable model for music audio classification based on prototype learning. Our model is based on a previous method, APNet, which jointly learns an autoen…
BUT CHiME-7 system description
Martin Karafiát, Karel Veselý, Igor Szöke +5
This paper describes the joint effort of Brno University of Technology (BUT), AGH University of Krakow and University of Buenos Aires on the development of Automatic Speech Recogni…
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…
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…
Study of positional encoding approaches for Audio Spectrogram Transformers
Leonardo Pepino, Pablo Riera, Luciana Ferrer
Transformers have revolutionized the world of deep learning, specially in the field of natural language processing. Recently, the Audio Spectrogram Transformer (AST) was proposed f…
Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings
Leonardo Pepino, Pablo Riera, Luciana Ferrer
Emotion recognition datasets are relatively small, making the use of the more sophisticated deep learning approaches challenging. In this work, we propose a transfer learning metho…
A Study on the Manifestation of Trust in Speech
Lara Gauder, Leonardo Pepino, Pablo Riera +4
Research has shown that trust is an essential aspect of human-computer interaction directly determining the degree to which the person is willing to use a system. An automatic pred…
Detecting Distrust Towards the Skills of a Virtual Assistant Using Speech
Leonardo Pepino, Pablo Riera, Lara Gauder +2
Research has shown that trust is an essential aspect of human-computer interaction directly determining the degree to which the person is willing to use the system. An automatic pr…
Trust-UBA: A Corpus for the Study of the Manifestation of Trust in Speech
Lara Gauder, Pablo Riera, Leonardo Pepino +4
This paper describes a novel protocol for collecting speech data from subjects induced to have different degrees of trust in the skills of a conversational agent. The protocol cons…