Publications

Found 304 results
[ Author(Desc)] Keyword Title Type Year
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Fomin T, Rozgonyi T, Szepesvári C, Lörincz A.  1996.  Self-Organizing Multi-Resolution Grid for Motion Planning and Control. Int. J. Neural Syst.. 7:757.
Forgács I., Gyimóthy T.  1996.  An Efficient Interprocedural Slicing Method for Large Programs. 9th International Conference on Software Engineering and Knowledge Engineering. :279-287.
French M., Szepesvari C., Rogers E..  1997.  Uncertainty, performance, and model dependency in approximate adaptive nonlinear control. Proceedings of the 36th IEEE Conference on Decision and Control. 3:3046-3051vol.3.
Fritzson P., Shahmehri N., Kamkar M., Gyimóthy T.  1992.  Generalized algorithmic debugging and testing. ACM Lett. Program. Lang. Syst.. 1:303–322.
Fritzson P, Gyimóthy T, Kamkar M, Shahmehri N.  1991.  Generalized Algorithmic Debugging and Testing. PLDI. :317–326.
Fülöp LJenő, Tóth G, Vidács L, Beszédes Á, Demeter H, Farkas L, Gyimóthy T, Balogh G.  2012.  Predictive Complex Event Processing: A conceptual framework for combining Complex Event Processing and Predictive Analytics. Proceedings of Fifth Balkan Conference in Informatics (BCI 2012). :26-31.
G
Gábor S, Gábor A, Nagy C, Ferenc R, Gyimóthy T.  2017.  Empirical Study on Refactoring Large-scale Industrial Systems and Its Effects on Maintainability. Journal of Systems and Software. 129:107–126.
Gergely T, Balogh G, Horváth F, Vancsics B, Beszédes Á, Gyimóthy T.  2018.  Analysis of Static and Dynamic Test-to-code Traceability Information. Acta Cybernetica. 23:903-919.
Gergely T, Balogh G, Horvath F, Vancsics B, Beszedes A, Gyimothy T.  2019.  Differences between a static and a dynamic test-to-code traceability recovery method. SOFTWARE QUALITY JOURNAL. 27:797-822.
Goldsmith J, Sloan RH, Turán G.  2002.  Theory Revision with Queries: DNF Formulas. Machine Learning. 47:257–295.
Gosztolya G, Kocsor A, Tóth L, Felföldi L.  2003.  Various Robust Search Methods in a Hungarian Speech Recognition System. Acta Cybernetica. 16:229-240.
Gosztolya G, Grósz T, Tóth L.  2016.  GMM-Free Flat Start Sequence-Discriminative DNN Training. Proceedings of Interspeech. :3409–3413.
Gosztolya G, Kocsor A.  2003.  Improving the Multi-stack Decoding Algorithm in a Segment-based Speech Recognizer. Proceedings of the 16th International Conference on Developments in Applied Artificial Intelligence. :744–749.
Gosztolya G, Kocsor A, Tóth L, Felföldi L.  2003.  Various Robust Search Methods in a Hungarian Speech Recognition System. Acta Cybernetica. 16:229–240.
Gosztolya G.  2019.  Using the Bag-of-Audio-Word Feature Representation of ASR DNN Posteriors for Paralinguistic Classification. Proceedings of Interspeech. :2413–2417.
Gosztolya G.  2019.  Using Fisher Vector and Bag-of-Audio-Words Representations to Identify Styrian Dialects, Sleepiness, Baby & Orca Sounds. Proceedings of Interspeech. :2413–2417.
Gosztolya G.  2020.  Using the Fisher Vector Representation for Audio-based Emotion Recognition. Acta Polytechnica Hungarica. 17:7–23.
Gosztolya G, Grósz T, Tóth L.  2020.  Social Signal Detection by Probabilistic Sampling DNN Training. IEEE Transactions on Affective Computing. 10:164–177.
Gosztolya G, Tóth L.  2019.  Calibrating DNN Posterior Probability Estimates of HMM/DNN Models to Improve Social Signal Detection From Audio Data. Proceedings of Interspeech. :515–519.
Gosztolya G.  2019.  Posterior-Thresholding Feature Extraction for Paralinguistic Speech Classification. Knowledge-Based Systems. 186
Gosztolya G, Grósz T, Tóth L, Markó A, Csapó TGábor.  2020.  Applying DNN Adaptation to Reduce the Session Dependency of Ultrasound Tongue Imaging-based Silent Speech Interfaces. Acta Polytechnica Hungarica. 17:109–124.
Gosztolya G, Busa-Fekete R, Grósz T, Tóth L.  2017.  DNN-Based Feature Extraction and Classifier Combination for Child-Directed Speech, Cold and Snoring Identification. Proceedings of Interspeech. :3522–3526.
Gosztolya G, Vincze V, Tóth L, Pákáski M, Kálmán J, Hoffmann I.  2019.  Identifying Mild Cognitive Impairment and mild Alzheimer’s disease based on spontaneous speech using ASR and linguistic features. Computer, Speech & Language. 53:181–197.
Gosztolya G, Busa-Fekete R.  2019.  Calibrating AdaBoost for Phoneme Classification. Soft Computing. 23:115–128.
Gosztolya G, Pintér Á, Tóth L, Grósz T, Markó A, Csapó TGábor.  2019.  Autoencoder-Based Articulatory-to-Acoustic Mapping for Ultrasound Silent Speech Interfaces. Proceedings of IJCNN.

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