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György Szaszák
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2020 – today
- 2023
- [c44]Kamel Nebhi, György Szaszák:
Automatic Assessment Of Spoken English Proficiency Based on Multimodal and Multitask Transformers. RANLP 2023: 769-776 - 2021
- [j8]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) - 2020
- [j7]Máté Ákos Tündik
, Balázs Tarján
A low latency sequential model and its user-focused evaluation for automatic punctuation of ASR closed captions. Comput. Speech Lang. 63: 101076 (2020) - [c43]Miklós Gábriel Tulics, György Szaszák, Krisztina Mészáros, Klára Vicsi:
Using ASR Posterior Probability and Acoustic Features for Voice Disorder Classification. CogInfoCom 2020: 155-160 - [c42]Balázs Tarján, György Szaszák, Tibor Fegyó, Péter Mihajlik
Improving Real-time Recognition of Morphologically Rich Speech with Transformer Language Model. CogInfoCom 2020: 491-496 - [c41]Balázs Tarján
On the Effectiveness of Neural Text Generation Based Data Augmentation for Recognition of Morphologically Rich Speech. TDS 2020: 437-445 - [i4]Balázs Tarján, György Szaszák, Tibor Fegyó, Péter Mihajlik:
On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech. CoRR abs/2006.05129 (2020) - [i3]Balázs Tarján, György Szaszák, Tibor Fegyó, Péter Mihajlik:
Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR. CoRR abs/2007.06949 (2020)
2010 – 2019
- 2019
- [j6]Máté Ákos Tündik, Valér Kaszás, György Szaszák:
On the Effects of Automatic Transcription and Segmentation Errors in Hungarian Spoken Language Processing. Period. Polytech. Electr. Eng. Comput. Sci. 63(4): 254-262 (2019) - [c40]Balázs Tarján, György Szaszák, Tibor Fegyó, Péter Mihajlik:
N-gram Approximation of LSTM Recurrent Language Models for Single-pass Recognition of Hungarian Call Center Conversations. CogInfoCom 2019: 131-136 - [c39]Miklós Gábriel Tulics, György Szaszák, Krisztina Mészáros, Klára Vicsi:
Artificial Neural Network and SVM based Voice Disorder Classification. CogInfoCom 2019: 307-312 - [c38]Máté Ákos Tündik, Valér Kaszás, György Szaszák:
Assessing the Semantic Space Bias Caused by ASR Error Propagation and its Effect on Spoken Document Summarization. INTERSPEECH 2019: 1333-1337 - [c37]György Szaszák, Máté Ákos Tündik:
Leveraging a Character, Word and Prosody Triplet for an ASR Error Robust and Agglutination Friendly Punctuation Approach. INTERSPEECH 2019: 2988-2992 - [c36]Bálint Döbrössy, Márton Makrai, Balázs Tarján
Investigating Sub-Word Embedding Strategies for the Morphologically Rich and Free Phrase-Order Hungarian. RepL4NLP@ACL 2019: 187-193 - [c35]Balázs Tarján
Investigation on N-Gram Approximated RNNLMs for Recognition of Morphologically Rich Speech. SLSP 2019: 223-234 - [i2]Balázs Tarján, György Szaszák, Tibor Fegyó, Péter Mihajlik:
Investigation on N-gram Approximated RNNLMs for Recognition of Morphologically Rich Speech. CoRR abs/1907.06407 (2019) - [i1]Dávid Sztahó, György Szaszák, András Beke:
Deep learning methods in speaker recognition: a review. CoRR abs/1911.06615 (2019) - 2018
- [j5]György Szaszák, Máté Ákos Tündik, Branislav Gerazov
Prosodic stress detection for fixed stress languages using formal atom decomposition and a statistical hidden Markov hybrid. Speech Commun. 102: 14-26 (2018) - [c34]Máté Ákos Tündik, György Szaszák:
Joint Word- and Character-level Embedding CNN-RNN Models for Punctuation Restoration. CogInfoCom 2018: 135-140 - [c33]Valér Kaszás, Máté Ákos Tündik, György Szaszák:
A semantic space approach for automatic summarization of documents. CogInfoCom 2018: 153-158 - [c32]Máté Ákos Tündik, György Szaszák, Gábor Gosztolya, András Beke:
User-centric Evaluation of Automatic Punctuation in ASR Closed Captioning. INTERSPEECH 2018: 2628-2632 - 2017
- [c31]Youssef Oualil, Dietrich Klakow, György Szaszák, Ajay Srinivasamurthy, Hartmut Helmke, Petr Motlícek
A context-aware speech recognition and understanding system for air traffic control domain. ASRU 2017: 404-408 - [c30]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 - [c29]Máté Ákos Tündik, Balázs Tarján
Á bilingual comparison of MaxEnt-and RNN-based punctuation restoration in speech transcripts. CogInfoCom 2017: 121-126 - [c28]Anna Moró, György Szaszák:
A prosody inspired RNN approach for punctuation of machine produced speech transcripts to improve human readability. CogInfoCom 2017: 219-224 - [c27]Anna Moró, György Szaszák:
A Phonological Phrase Sequence Modelling Approach for Resource Efficient and Robust Real-Time Punctuation Recovery. INTERSPEECH 2017: 558-562 - [c26]Ajay Srinivasamurthy, Petr Motlícek
Semi-Supervised Learning with Semantic Knowledge Extraction for Improved Speech Recognition in Air Traffic Control. INTERSPEECH 2017: 2406-2410 - [c25]Máté Ákos Tündik, Balázs Tarján
Low Latency MaxEnt- and RNN-Based Word Sequence Models for Punctuation Restoration of Closed Caption Data. SLSP 2017: 155-166 - 2016
- [c24]Máté Ákos Tündik, Branislav Gerazov
Atom decomposition based stress detection and automatic phrasing of speech. CogInfoCom 2016: 25-30 - [c23]György Szaszák, Máté Ákos Tündik, András Beke:
Summarization of Spontaneous Speech using Automatic Speech Recognition and a Speech Prosody based Tokenizer. KDIR 2016: 221-227 - [c22]Gábor Gosztolya, Tamás Grósz
Estimating the Sincerity of Apologies in Speech by DNN Rank Learning and Prosodic Analysis. INTERSPEECH 2016: 2026-2030 - [c21]András Beke, György Szaszák:
Automatic Summarization of Highly Spontaneous Speech. SPECOM 2016: 140-147 - [c20]György Szaszák, Máté Ákos Tündik, Branislav Gerazov
Combining Atom Decomposition of the F0 Track and HMM-based Phonological Phrase Modelling for Robust Stress Detection in Speech. SPECOM 2016: 165-173 - [c19]Milan Secujski, Branislav Gerazov
Design of a Speech Corpus for Research on Cross-Lingual Prosody Transfer. SPECOM 2016: 199-206 - [c18]Bálint Pál Tóth, Kornél István Kis, György Szaszák, Géza Németh
Ensemble Deep Neural Network Based Waveform-Driven Stress Model for Speech Synthesis. SPECOM 2016: 271-278 - 2015
- [c17]György Szaszák, András Beke, Gábor Olaszy, Bálint Pál Tóth:
Using automatic stress extraction from audio for improved prosody modelling in speech synthesis. INTERSPEECH 2015: 2227-2231 - [c16]Ádam Varga, Balázs Tarján
Automatic Close Captioning for Live Hungarian Television Broadcast Speech: A Fast and Resource-Efficient Approach. SPECOM 2015: 105-112 - [c15]György Szaszák, András Beke:
Toward Exploring the Role of Disfluencies from an Acoustic Point of View: A New Aspect of (Dis)continuous Speech Prosody Modelling. TSD 2015: 369-377 - 2014
- [c14]András Beke, György Szaszák:
Combining NLP techniques and acoustic analysis for semantic focus detection in speech. CogInfoCom 2014: 493-497 - 2013
- [c13]György Szaszák, Philip N. Garner
Evaluating intra- and crosslingual adaptation for non-native speech recognition in a bilingual environment. CogInfoCom 2013: 357-362 - [c12]András Beke, György Szaszák, Viola Varadi
Automatic phrase segmentation and clustering in spontaneous speech. CogInfoCom 2013: 459-462 - [c11]György Szaszák, András Beke:
Using phonological phrase segmentation to improve automatic keyword spotting for the highly agglutinating Hungarian language. INTERSPEECH 2013: 1589-1593 - 2012
- [j4]György Szaszák, András Beke:
Exploiting Prosody for Syntactic Analysis in Automatic Speech Understanding. J. Lang. Model. 0(1): 143-172 (2012) - [c10]György Szaszák, András Beke:
Automatic prosodic and syntactic analysis from speech in cognitive infocommunication. CogInfoCom 2012: 377-382 - [c9]András Beke, György Szaszák:
Unsupervised Clustering of Prosodic Patterns in Spontaneous Speech. TSD 2012: 648-655 - 2011
- [j3]Fabien Ringeval, Jean Demouy, György Szaszák, Mohamed Chetouani
Automatic Intonation Recognition for the Prosodic Assessment of Language-Impaired Children. IEEE Trans. Speech Audio Process. 19(5): 1328-1342 (2011) - [c8]György Szaszák, Katalin Nagy, András Beke:
Analysing the Correspondence Between Automatic Prosodic Segmentation and Syntactic Structure. INTERSPEECH 2011: 1057-1060 - 2010
- [j2]Klára Vicsi, György Szaszák:
Using prosody to improve automatic speech recognition. Speech Commun. 52(5): 413-426 (2010)
2000 – 2009
- 2009
- [b1]György Szaszák:
A szupraszegmentális jellemzők szerepe és felhasználása a gépi beszédfelismerésben. Budapest University of Technology and Economics, Hungary, 2009 - [c7]György Szaszák, David Sztahó, Klára Vicsi:
Automatic intonation classification for speech training systems. INTERSPEECH 2009: 1899-1902 - 2008
- [c6]Klára Vicsi, György Szaszák:
Using prosody for the improvement of ASR - sentence modality recognition. INTERSPEECH 2008: 2877-2880 - 2007
- [c5]György Szaszák, Klára Vicsi:
Using Prosody in Fixed Stress Languages for Improvement of Speech Recognition. COST 2102 Workshop (Vietri) 2007: 138-149 - [c4]György Szaszák, Klára Vicsi:
Speech Recognition Supported by Prosodic Information for Fixed Stress Languages. TSD 2007: 262-269 - 2006
- [c3]Klára Vicsi, György Szaszák:
Prosodic Cues for Automatic Phrase Boundary Detection in ASR. TSD 2006: 547-554 - 2005
- [j1]Klára Vicsi, György Szaszák:
Automatic Segmentation of Continuous Speech on Word Level Based on Supra-segmental Features. Int. J. Speech Technol. 8(4): 363-370 (2005) - 2004
- [c2]Andrej Zgank, Zdravko Kacic, Frank Diehl, Klára Vicsi, György Szaszák, Jozef Juhár, Slavomír Lihan:
The COST 278 MASPER Initiative - Crosslingual Speech Recognition with Large Telephone Databases. LREC 2004 - [c1]György Szaszák, Klára Vicsi:
Examination of Pronunciation Variation from Hand-Labelled Corpora. TSD 2004: 473-480
Coauthor Index
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