G
Gábor Gosztolya
Researcher at University of Szeged
Publications - 113
Citations - 1228
Gábor Gosztolya is an academic researcher from University of Szeged. The author has contributed to research in topics: Computer science & Hidden Markov model. The author has an hindex of 14, co-authored 93 publications receiving 881 citations. Previous affiliations of Gábor Gosztolya include Hungarian Academy of Sciences.
Papers
More filters
Journal ArticleDOI
A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech.
László Tóth,Ildikó Hoffmann,Gábor Gosztolya,Veronika Vincze,Gréta Szatlóczki,Zoltán Bánréti,Magdolna Pákáski,János Kálmán +7 more
TL;DR: The temporal analysis of spontaneous speech can be exploited in implementing a new, auto-matic detection-based tool for screening MCI for the community.
Journal ArticleDOI
Identifying Mild Cognitive Impairment and mild Alzheimer’s disease based on spontaneous speech using ASR and linguistic features
Gábor Gosztolya,Veronika Vincze,László Tóth,Magdolna Pákáski,János Kálmán,Ildikó Hoffmann,Ildikó Hoffmann +6 more
TL;DR: An automatic speech recognition based procedure for the extraction of a special set of acoustic features and a linguistic feature set that is extracted from the transcripts of the same speech signals to tell apart Alzheimer’s patients from those with mild cognitive impairment.
Proceedings ArticleDOI
Automatic detection of Mild cognitive impairment from spontaneous speech using ASR
László Tóth,Gábor Gosztolya,Veronika Vincze,Ildikó Hoffmann,Ildikó Hoffmann,Gréta Szatlóczki,Edit Biro,Fruzsina Zsura,Magdolna Pákáski,János Kálmán +9 more
TL;DR: This work automates the extraction of the features of Mild Cognitive Impairment by applying automatic speech recognition (ASR), and uses machine learning methods to separate the subjects with MCI from the control group.
Proceedings ArticleDOI
DNN-Based Ultrasound-to-Speech Conversion for a Silent Speech Interface.
TL;DR: It is found that the representation that used several neighboring image frames in combination with a feature selection method was preferred both by the subjects taking part in the listening experiments, and in terms of the Normalized Mean Squared Error.
Proceedings Article
Cross-lingual Portability of MLP-Based Tandem Features - A Case Study for English and Hungarian
TL;DR: This work examines the portability of feature extractor MLPs between an Indo-European and a Finno-Ugric language and finds that the cross-lingual configurations achieve similar performance to the monolingual system, and that the AF detectors lead to slightly worse performance, despite the expectation that they should be more language-independent than phone-based MLPs.