Speaker: Ana Belén Fernández Cordero. Abstract: Air traffic control (ATC) relies on communication via speech between pilot and air-traffic controller (ATCO). The call-sign, as unique identifier for each flight, is used to address a specific pilot by the ATCO. Extracting… Read More
AVASpeech-SMAD: A speech and music activity detection database with label co-occurrence
Speaker: Guillermo Recio Martín. Abstract: AVASpeech is a publicly available dataset created in 2018 to contribute to the task of speech activity detection (SAD) task. This dataset contains three different types of audio segments: clean speech, speech co-occuring with music… Read More
Conformer-based sound event detection with semi-supervised learning and data augmentation
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 Diarization with Region Proposal Network
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
Conversational Agents for Health Care
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
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
Efficient Neural Approaches for Automatic Speech Recognition
Speaker: Doroteo Torre Toledano Abstract: Many different end-to-end neural approaches have been proposed in the last years in the field of automatic speech recognition (ASR). However, most of the research available compares systems only in terms of accuracy (word error… Read More