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, 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, 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.

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, 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, Tóth L.  2018.  A feature selection-based speaker clustering method for paralinguistic tasks. Pattern Analysis and Applications. 21:193–204.

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, 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.  2019.  Calibrating AdaBoost for Phoneme Classification. Soft Computing. 23:115–128.

Gosztolya G.  2019.  Posterior-Thresholding Feature Extraction for Paralinguistic Speech Classification. Knowledge-Based Systems. 186

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.

Goldsmith J, Sloan RH, Turán G.  2002.  Theory Revision with Queries: DNF Formulas. Machine Learning. 47:257–295.

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.

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.