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2020 – today
- 2024
[j6]Dosti Aziz
, Dávid Sztahó:
Automatic cross- and multi-lingual recognition of dysphonia by ensemble classification using deep speaker embedding models. Expert Syst. J. Knowl. Eng. 41(10) (2024)- [j5]Mohammed Hamzah Abed, Dávid Sztahó:
Effect of identical twins on deep speaker embeddings based forensic voice comparison. Int. J. Speech Technol. 27(2): 341-351 (2024) - [c31]Dosti Aziz, Dávid Sztahó:
Binary and Multiclass Classification of Dysphonia Using Whisper Encoder and One-Dimensional Convolutional Neural Network. SPECOM (1) 2024: 352-366 - [c30]Dosti Aziz, Dávid Sztahó:
Dysphonia Diagnosis Using Self-supervised Speech Models in Mono and Cross-Lingual Settings. TSD (2) 2024: 24-35 - [c29]Mohammed Hamzah Abed, Dávid Sztahó:
Deep Speaker Embeddings for Speaker Verification of Children. TSD (2) 2024: 58-69 - 2023
- [c28]Daria Hemmerling, Marek Wodzinski, Juan Rafael Orozco-Arroyave, David Sztahó, Mateusz Daniol, Pawel Jemiolo, Magdalena Wójcik-Pedziwiatr:
Vision Transformer for Parkinson's Disease Classification using Multilingual Sustained Vowel Recordings. EMBC 2023: 1-4 - 2022
- [c27]Dávid Sztahó, Miklós Gábriel Tulics, Jinzi Qi, Hugo Van hamme, Klára Vicsi:
Cross-lingual Detection of Dysphonic Speech for Dutch and Hungarian Datasets. BIOSIGNALS 2022: 215-220 - [c26]José Vicente Egas López, Gábor Kiss, Dávid Sztahó, Gábor Gosztolya:
Automatic Assessment of the Degree of Clinical Depression from Speech Using X-Vectors. ICASSP 2022: 8502-8506 - [c25]Attila Zoltán Jenei, Gábor Kiss, Dávid Sztahó:
Detection of Speech Related Disorders by Pre-trained Embedding Models Extracted Biomarkers. SPECOM 2022: 279-289 - [i2]Dávid Sztahó, Attila Fejes:
Effects of language mismatch in automatic forensic voice comparison using deep learning embeddings. CoRR abs/2209.12602 (2022) - 2021
- [j4]Dávid Sztahó, György Szaszák, András Beke:
Deep Learning Methods in Speaker Recognition: A Review. Period. Polytech. Electr. Eng. Comput. Sci. 65(4): 310-328 (2021) - [c24]Dávid Sztahó, Gábor Kiss, Miklós Gábriel Tulics:
Deep Learning Solution for Pathological Voice Detection using LSTM-based Autoencoder Hybrid with Multi-Task Learning. BIOSIGNALS 2021: 135-141 - [c23]Gábor Kiss, Dávid Sztahó, Miklós Gábriel Tulics:
Application for Detecting Depression, Parkinson's Disease and Dysphonic Speech. Interspeech 2021: 956-957 - 2020
- [c22]David Sztahó, Orosz Anett, István Valálik:
Articulation correctness measurement of Parkinson's disease using low resource-intensitve segmentation methods. CogInfoCom 2020: 121-124
2010 – 2019
- 2019
- [c21]Dávid Sztahó, István Valálik:
Speech Fluency Measurement of Patients with Parkinson's Disease by Forward-Backward Divergence Segmentation. CogInfoCom 2019: 295-300 - [c20]Gábor Kiss, Dávid Sztahó, Klára Vicsi:
Depression State Assessment: Application for Detection of Depression by Speech. INTERSPEECH 2019: 966-967 - [c19]Dávid Sztahó, Gábor Kiss, Miklós Gábriel Tulics, Bálint Hajduska-Dér, Klára Vicsi:
Automatic discrimination of several types of speech pathologies. SpeD 2019: 1-6 - [c18]David Sztahó, István Valálik, Klára Vicsi:
Parkinson's Disease Severity Estimation on Hungarian Speech Using Various Speech Tasks. SpeD 2019: 1-6 - [i1]Dávid Sztahó, György Szaszák, András Beke:
Deep learning methods in speaker recognition: a review. CoRR abs/1911.06615 (2019) - 2018
- [j3]David Sztahó, Gábor Kiss, Klára Vicsi:
Computer based speech prosody teaching system. Comput. Speech Lang. 50: 126-140 (2018) - [c17]Gábor Kiss, Artur Bendeguz Takacs, Dávid Sztahó, Klára Vicsi:
Detection Possibilities of Depression and Parkinson's disease Based on the Ratio of Transient Parts of the Speech. CogInfoCom 2018: 165-168 - [c16]David Sztahó, Gábor Kiss, Miklós Gábriel Tulics, Klára Vicsi:
Automatic Separation of Various Disease Types by Correlation Structure of Time Shifted Speech Features. TSP 2018: 1-4 - 2017
- [c15]David Sztahó, Miklós Gábriel Tulics, Klára Vicsi, István Valálik:
Automatic estimation of severity of Parkinson's disease based on speech rhythm related features. CogInfoCom 2017: 11-16 - [c14]Máté Ákos Tündik, Gábor Kiss, David Sztahó, György Szaszák:
Assessment of pathological speech prosody based on automatic stress detection and phrasing approaches. CogInfoCom 2017: 67-72 - 2016
- [c13]David Sztahó, Klára Vicsi:
Estimating the Severity of Parkinson's Disease Using Voiced Ratio and Nonlinear Parameters. SLSP 2016: 96-107 - [p1]Gábor Kiss, Miklós Gábriel Tulics, Dávid Sztahó, Anna Esposito, Klára Vicsi:
Language Independent Detection Possibilities of Depression by Speech. Recent Advances in Nonlinear Speech Processing 2016: 103-114 - 2015
- [c12]David Sztahó, Gábor Kiss, Klára Vicsi:
Estimating the severity of parkinson's disease from speech using linear regression and database partitioning. INTERSPEECH 2015: 498-502 - 2014
- [b1]Dávid Sztahó:
Automatikus érzelem-felismerés akusztikai paraméterek alapján. Budapest University of Technology and Economics, Hungary, 2014 - [j2]David Sztahó, Klára Vicsi:
Speech activity detection and automatic prosodic processing unit segmentation for emotion recognition. Intell. Decis. Technol. 8(4): 315-324 (2014) - [c11]Gábor Kiss, Dávid Sztahó, Klára Vicsi, Adrianos Golemis:
Connection between body condition and speech parameters - especially in the case of hypoxia. CogInfoCom 2014: 333-336 - [c10]David Sztahó, Gábor Kiss, László Czap, Klára Vicsi:
A computer-assisted prosody pronunciation teaching system. WOCCI 2014: 45-49 - 2013
- [c9]Gábor Kiss, David Sztahó, Klára Vicsi:
Language independent automatic speech segmentation into phoneme-like units on the base of acoustic distinctive features. CogInfoCom 2013: 579-582 - 2012
- [j1]Klára Vicsi, David Sztahó:
Recognition of Emotions on the Basis of Different Levels of Speech Segments. J. Adv. Comput. Intell. Intell. Informatics 16(2): 335-340 (2012) - [c8]Klára Vicsi, Dávid Sztahó, Gábor Kiss:
Examination of the sensitivity of acoustic-phonetic parameters of speech to depression. CogInfoCom 2012: 511-515 - 2010
- [c7]David Sztahó, Viktor Imre, Klára Vicsi:
Automatic Classification of Emotions in Spontaneous Speech. COST 2102 Conference 2010: 229-239 - [c6]Klára Vicsi, David Sztahó:
Problems of the Automatic Emotion Recognitions in Spontaneous Speech; An Example for the Recognition in a Dispatcher Center. COST 2102 Training School 2010: 331-339
2000 – 2009
- 2009
- [c5]David Sztahó, Katalin Nagy, Klára Vicsi:
Subjective Tests and Automatic Sentence Modality Recognition with Recordings of Speech Impaired Children. COST 2102 Training School 2009: 397-405 - [c4]György Szaszák, David Sztahó, Klára Vicsi:
Automatic intonation classification for speech training systems. INTERSPEECH 2009: 1899-1902 - 2008
- [c3]David Sztahó, Katalin Nagy, Klára Vicsi:
Automatic Sentence Modality Recognition in Children's Speech, and Its Usage Potential in the Speech Therapy. COST 2102 Conference (Prague) 2008: 266-275 - [c2]Fabien Ringeval, Mohamed Chetouani, David Sztahó, Klára Vicsi:
Automatic prosodic disorders analysis for impaired communication children. WOCCI 2008: 3 - 2007
- [c1]Szabolcs Levente Tóth, David Sztahó, Klára Vicsi:
Speech Emotion Perception by Human and Machine. COST 2102 Workshop (Patras) 2007: 213-224
Coauthor Index
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