Author: daniel

Speaker: Sara Barahona Quirós. Abstract: This paper presents a Conformer-based sound event detection (SED) method, which uses semi-supervised learning and data augmentation. The proposed method employs Conformer, a convolution-augmented Transformer that is able to exploit local features of audio data… Read More

Speaker: Sergio Izquierdo del Álamo. Abstact: Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the “who spoke when” problem. Although the standard diarization systems can achieve satisfactory results in various scenarios, they… Read More

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

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

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

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