In this study, we investigated the effects of changes in formant structure of externally presented speech signals on participants’ auditory perception of their own speech output (i.e., feedback) during a word production task. The study involved a novel combination of two previously established research paradigms: (1) sensorimotor adaptation to altered auditory feedback during speech, and (2) ex...

Active learning is useful in situations where labeled data is scarce, unlabeled data is available, and labeling a large number of examples is costly or impractical. These techniques help by identifying a minimal set of examples to label that will support the training of an effective classifier. Thus active learning is particularly relevant for the automation of annotation tasks in multimedia. I...

This paper describes the I3A-University of Zaragoza speaker recognition system in the Evalita 2009 Speaker Identity Verification Application task. Two systems have been submitted for evaluation: a primary system based on Joint Factor Analysis and a secondary system based on classical MAP adaptation without any channel compensation for comparison. We present results for both systems on the devel...

The quality of a spoken term detection (STD) system critically depends on the choice of a “thresholding” function, which is used to determine whether to output a candidate detection or not based on its score. In the context of the IARPA Babel program and the NIST OpenKWS evaluation series, the penalty for missing an occurrence depends on the frequency of the keyword, so it is desirable either t...

We investigate a range of music recommendation algorithm combinations, score aggregation functions, normalization techniques, and late fusion techniques on approximately 200 million listening events collected through Last.fm. The overall goal is to identify superior combinations for the task of artist recommendation. Hypothesizing that user characteristics influence performance on these algorit...

This paper addresses the problem of detecting keywords in unconstrained speech. The proposed algorithms search for the speech segment maximizing the average observation probability along the most likely path in the hypothesized keyword model. As known, this approach (sometimes referred to as sliding model method) requires a relaxation of the begin/endpoints of the Viterbi matching, as well as a...

COVID-19 is an infectious disease that causes acute respiratory distress syndrome due to the SARS-CoV-2 virus. Rapid and accurate screening early diagnosis of patients play essential role in controlling outbreaks reducing spread this disease. This can be diagnosed by manually reading CXR images, but it time-consuming prone errors. For reason, research proposes automatic medical image segmentati...