Speaker: Giuliano Lazzara. Abstract: Brief that focuses on people’s perception of Conversational Agents and proposes these technologies as a tool to deal with underestimated mental issues such as depression and anxiety. Referring to experiments done with “Woebot”, an automated conversational… Read More
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
Speaker: Sergio Segovia. Abstract: The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such… Read More
Data Augmentation for Decoupled Calibration of Deep Neural Network Classifiers
Speaker: Sergio Márquez Carrero. Abstract: Modern Deep Neural Networks (DNN) have significantly outperformed those employed over a decade ago in terms of accuracy. Nonetheless, the outputs generated by these models are poorly calibrated, causing substantial issues in a variety of… Read More
Connectionist Temporal Classification (CTC) Speech Segmentation
Speaker: W. Fernando López Gavilanez. Abstract: Motivated by the lack of high-quality labeled data for specific scenarios, such as emergencies in the home environment, we explored a CTC-segmentation method to generate a specific-purpose speech dataset. The project seeks the quality improvement of… Read More
Beltrán Labrador Selected for a Summer Internship at Google Research NY
Beltrán Labrador Serrano has been recently selected for a summer internship at the Speech Processing group of Google Research in New York, USA. He will be starting the internship in May and returning to Madrid in September.
BigSSL: Large-Scale Semi-Supervised Learning for ASR
Speaker: Laura Herrera Abstract: This paper deals with results obtained on very large automatic speaker recognition models.A large amount of labelled data is not always available and sometimes they do not generalize enough. Consequently, the authors propose to use pre-trained… Read More