← Search

José Vicente Egas López

3 accepted papers

2022

Automatic Assessment of the Degree of Clinical Depression from Speech Using X-Vectors

ICASSP 2022accepted

Depression is a frequent and curable psychiatric disorder, detrimentally affecting daily activities, harming both work-place productivity and personal relationships. Among many other symptoms, depression is associated with disordered speech production, which might permit its automatic screening by m…

Cited by 0SourceScholar
2022

Using Spectral Sequence-to-Sequence Autoencoders to Assess Mild Cognitive Impairment

ICASSP 2022accepted

Dementia is a chronic or progressive clinical syndrome, mainly characterized by the deterioration of memory, thinking, reasoning and language. In Mild cognitive impairment (MCI), often considered as the prodromal stage of dementia, there is also a subtle deterioration of these functions, but they do…

Cited by 0SourceScholar
2021

Deep Neural Network Embeddings for the Estimation of the Degree of Sleepiness

ICASSP 2021accepted

Estimating the degree of sleepiness from the human speech is an emerging research problem with straightforward applications. In this study, we employ the x-vector approach, currently the state-of-the-art in speaker recognition, as a neural network feature extractor to detect the level of sleepiness…

Cited by 0SourceScholar