155, László Tóth, Amin Honarmandi Shandiz, Gábor Gosztolya, Tamás Gábor Csapó: Adaptation of Tongue Ultrasound-Based Silent Speech Interfaces Using Spatial Transformer Networks Proc. Interspeech 2023, 1169-1173, Dublin, Ireland, 2023.

154, G. Gosztolya, László Tóth, V. Svindt, J. Bóna, I. Hoffmann: Using Acoustic Deep Neural Network Embeddings To Detect Multiple Sclerosis From Speech, Proc. ICASSP, pp. 6927-6931, 2022.

153, Amin Honarmandi Shandiz, László Tóth: Improved processing of ultrasound tongue videos by combining ConvLSTM and 3D convolutional networks, Proc. IEA/AIE, Springer LNAI Vol. 13343, pp. 265-274, 2022.

152, Terbe, D., Tóth, L., Ivaskó, L., Hangkonverzió alkalmazása dysarthriás betegek beszédminőségének javítására (in Hungarian), Proceedings of MSZNY 2022, pp. 161-174, Szeged, Hungary, 2022.

151, Gosztolya, G., Tóth, L., Svindt, V., Bóna, J., Hoffmann, I.: Sclerosis Multiplex hangalapú felismerése akusztikai alapú beágyazások használatával (in Hungarian), Proceedings of MSZNY 2022, pp. 151-160, Szeged, Hungary, 2022.

150, Vetráb, M., Egas-López, J.V., Balogh, R., Imre, N., Hoffmann, I., Tóth, L., Pákáski, M., Kálmán, J., Gosztolya, G.: Enyhe kognitív zavar automatikus felismerése szekvenciális autoenkóder használatával (in Hungarian), Proceedings of MSZNY 2022, pp. 175-184, Szeged, Hungary, 2022.

149, Egas-López, J.V., Balogh, R., Imre, N., Hoffmann, I., Szabó, M.K., Tóth, L., Pákáski, M., Kálmán, J., Gosztolya, G.: Automatic Screening of Mild Cognitive Impairment and Alzheimer's Disease by Means of Posterior-Thresholding Hesitation Representation, Computer, Speech & Language, Vol. 75, article no. 101377, 2022.

148, Imre, N., Balogh, R., Gosztolya, G., Tóth, L., Hoffmann, I., Várkonyi, T., Lengyel, Cs., Pákáski, M., Kálmán, J.: Temporal Speech Parameters Indicate Early Cognitive Decline in Elderly Patients With Type 2 Diabetes Mellitus, Alzheimer Disease & Associated Disorders, Vol. 36, No. 2, pp. 148-155, 2022.

147, Csapó, T.G., Gosztolya, G., Tóth, L., Shandiz, A.H., Markó, A.: Optimizing the Ultrasound Tongue Image Representation for Residual Network-Based Articulatory-to-Acoustic Mapping, Sensors, Vol. 22, article no. 8601, 2022.

146, G. Pap, K. Adam, Z. Gyorgypal, L. Toth. and Z. Hegedus. Depthwise Convolutions using Physicochemical Features of DNA for Transcription Factor Binding Site Classification. In The 6th International Conference on Advances in Artificial Intelligence (ICAAI 2022), ACM, 00, 2022.

145, Kálmán, J., Devanand, D.P, Gosztolya, G., Balogh, R., Imre, N., Tóth, L., Hoffmann, I., Kovács, I., Vincze, V., Pákáski, M.: Temporal speech parameters detect mild cognitivwe impairment in different languages: validation and comparison of the Speech-GAP test in English and Hungarian, Current Alzheimer Research 2022.

144, Vincze, V., Szabó. M.K., Hoffmann, I., Tóth, L., Pákáski, M., Kálmán, J., Gosztolya, G.: Linguistic Parameters of Spontaneous Speech for identifying Mild Cognitive Impairment and Alzheimer’s Disease, Computational Linguistics, 2022.

143, Shandiz, A.H., Tóth, L.: Voice Activity Detection for Ultrasound-based Silent Speech Interfaces using Convolutional Neural Networks, Proc. TSD 2021.

142, Zainkó, Cs., Tóth, L., Shandiz, A.H., Gosztolya, G., Markó, A., Németh, G., Csapó, T.G.: Adaptation of Tacotron2-based Text-To-Speech for Articulatory-to-Acoustic Mapping using Ultrasound Tongue Imaging, Proc. Speech Synthesis Workshop, 2021.

141, Csapó, T.G., Tóth, L., Gosztolya, G., Markó, A.: Speech Synthesis from Text and Ultrasound Tongue Image-based Articulatory Input, Proc. Speech Synthsis Workshop, 2021.

140, Egas-Lopez, J.V., Vetráb, M., Tóth, L., Gosztolya, G.: Identifying Conflict Escalation and Primates by Using Ensemble X-Vectors and Fisher Vector Features, Proc. Interspeech 2021.

139, Shandiz, A.H, Tóth, L., Gosztolya, G., Markó, A., Csapó, T.G.: Neural Speaker Embedding for Ultrasound-based Silent Speech Interfaces, Proc. Interspeech 2021.

138, Yu Y., Shandiz, A.H., Tóth, L.: Reconstructing Speech from Real-Time Articulatory MRI Using Neural Vocoders, Proc. EUSIPCO, 2021.

137, Shandiz A.H., Tóth L., Gosztolya G., Markó A., Csapó T. G.: Improving Neural Silent Interface Speech Models by Adversarial Training, Proc. AICV 2021, pp. 430-440.

136, Tóth László, Amin Honarmandi Shandiz, Gosztolya Gábor, Zainkó Csaba, Markó Alexandra, Csapó Tamás Gábor, 3D konvolúciós neuronhálón és neurális vokóderen alapuló némabeszéd-interfész, Proc. MSZNY 2021, pp. 123-138.

135, László Tóth, Amin Honarmandi Shandiz: 3D Convolutional Neural Networks for Ultrasound-Based Silent Speech Interfaces, Proc. ICAISC, pp. 159-169, 2020.

134, Vincze, V., Szatlocki, G., Tóth, L., Gosztolya, G., Pakaski, M., Hoffmann, I., Kalman, J.: Telltale silence: temporal speech parameters discriminate between prodromal dementia and mild Alzheimer’s disease, Clinical Linguistics and Phonetics, 2020.

133, Gosztolya, G., Toth, L.: Applying Speech Tempo-Derived Features, BoAW and Fisher Vectors to Detect Elderly Emotion and Speech in Surgical Masks, Proc. Interspeech 2020

132, Gosztolya, G., Grosz, T., Toth, L., Marko, A., Csapo, T.: Applying DNN Adaptation to Reduce the Session Dependency of Ultrasound Tongue Imaging-based Silent Speech Interfaces, Acta Polytechnica Hungarica, Vo. 17, No. 7, 2020.

131, Imre, N., Balogh, R., Gosztolya, G., Tóth, L., Várkonyi, T., Lengyel, Cs., Pákáski, M., Kálmán, J.: Automatic recognition of temporal speech features in type 2 diabetes mellitus with mild cognitive impairment,Proc. CogInfoCom 2019.

130, Pap, G., Toth, L.: A Comparison of Supervised and Semi-supervised Training Algorithms of Restricted Boltzmann Machines on Biological Data, Proc. CINTI-MACRO 2019.

129, Toth, L., Gosztolya, G., Reducing the Inter-Speaker Variance of CNN Acoustic Models Using Unsupervised Adversarial Multi-Task Training, Proc. SPECOM 2019, pp. 481-490, 2019.

128, Lopez, H.V.E., Toth, L., Hoffmann, I., Kalman, J., Pakaski, M., Gosztolya, G., Assessing Alzheimer's Disease from Speech Using the i-Vector Approach, Proc. SPECOM 2019, pp. 289-298, 2019.

127, Kovacs, Gy., Toth, L., Van Compernolle, D., Liwicki, M.:Examining the combination of multi-band processing and channel dropout for robust speech recognition, Proc. Interspeech 2019.

126, Gosztolya, G., Toth, L.: Calibrating DNN Posterior Probability Estimates of HMM/DNN Models to Improve Social Signal Detection From Audio Data, Proc. Interspeech 2019.

125, Csapo, T., Al-Radhi, M., Nemeth, G., Gosztolya, G., Grosz, T., Toth, L., Marko, A.: Ultrasound-based Silent Speech Interface Built on a Continuous Vocoder, Proc. Interspeech 2019.

124, Gosztolya, G., Pinter, A., Toth, L., Grosz, T., Marko, A., Csapo, T.: Autoencoder-Based Articulatory-to-Acoustic Mapping for Ultrasound Silent Speech Interfaces, Proc. IJCNN, 2019.

123, Toth, L., Gosztolya, G.: Adversarial Multi-Task Learning of Speaker-Invariant Deep Neural Network Acoustic Models for Speech Recognition, Proc. ASPAI 2019, pp. 82-86.

122, Grosz, T., Toth, L.: Mely neuronhalos beszedfelismerok mukodesenek ertelmezo elemzese, Proc. MSZNY 2019, pp. 287-297

121, Pinter, A., Gosztolya, G., Toth, L., Grosz, T., Csapo, T., Marko, A.: Autoenkoderen alapulo jellemzoreprezentacio mely nauronhalos, ultrahang-alapu nemabeszed-interfeszekben, Proc. MSZNY 2019, pp. 13-22.

120, Toth, L., Gosztolya, G.: Beszeloinvarians akusztikus modellek letrehozasa mely neuronhalok ellenseges multi-taszk tanitasaval, Proc. MSZNY 2019, pp. 3-11.

119, Kov?s, Gy., T?h, L., Gosztolya, G.: Multi-Band Processing with Gabor Filters and Time-Delay Neural Nets for Noise Robust Speech Recognition, Proc. SLT 2018, pp.

118, T?h, L., Kov?s, Gy., Ivask? L., T?h, A., Jakab, K., V?sei, L.: Stroke-on ?esett dysarthir? betegek besz??ek g?i elemz?e - kezdeti eredm?yek, Proc. Neumann Kollokvium, 2018, pp. 43-49.

117, T?h, L., Kov?s, Gy., Van Compernolle, D.: A Perceptually Inspired Data Augmentation Method for Noise Robust CNN Acoustic Models, Proc. SPECOM 2018, pp. 697-706.

116, Gosztolya, G., Grosz, T., Toth, L.: General Utterance-Level Feature Extraction for Classifying Crying Sounds, Atypical & Self-Assessed Affect and Heart Beats, Proc. Interspeech, pp. 531-535, 2018.

115, Toth, L., Gosztolya, G., Grosz, T., Marko, A., Csapo, T.: Multi-Task Learning of Speech Recognition and Speech Synthesis Parameters for Ultrasound-based Silent Speech Interfaces, Proc. Interspeech, pp. 3172-3176, 2018.

114, Gosztolya, G., Grosz, T., Toth, L.: Social Signal Detection by Probabilistic Sampling DNN Training, IEEE Transactions on Affective Computing, Vol. 11, No. 1, pp. 164-177, 2020.

113, Gosztolya, G., Vincze, V., T?h, L., P??ki, M., K?m?, J., Hoffmann, I.:Identifying Mild Cognitive Impairment and Mild Alzheimer's Disease Based On Spontaneous Speech Using ASR and Linguistic Features, Computer Speech and language, 53: pp. 181-197. (2019)

112, Gr?z, T., Gosztolya, G., T?h, L., Csap? T., Mark? A.: F0 Estimation for DNN-Based Ultrasound Silent Speech Interfaces, Proc. ICASSP 2018, pp. 291-295.

111, Toth, L.: Deep Neural Network with Linearly Augmented Rectifier Layers for Speech Recognition, Proc. SAMI 2018, pp. 189-193, 2018.

110, Gosztolya, G., Hoffmann, I., T?h, L., Vincze, V., P??ki, M., K?m?, J.: Az enyhe kognit? zavar ? a korai Alzheimer-k? automatikus azonos??a spont? besz?b? akusztikus jellemz? seg?s??el, Proc. MSZNY, 2018.

109, Gr?z, T., T?h, L., Gosztolya, G., Csap? T., Mark? A.: K??letek az alapfrekvencia becsl??e m?y neuronh??, ultrahang alap? n?abesz?-interf?zekben, Proc. MSZNY, 2018.

108, T?h, L., Gr?z, T., GOsztolya, G.: Besz?felismer? m?y neuronh?? ?lapotkapcsol?i algoritmusainak k??leti ?szehasonl??a, Proc. MSZNY. 2018.

107, Toth, L., Hoffmann, I., Gosztolya, G., Vincze, V., Szatloczki, G., Banreti, Z., Pakaski, M., Kalman, J.: A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech, Current Alzheimer Research, Vol. 15, No. 2, pp. 130-138, 2018.

106, Hoffmann, I., T?h, L., Gosztolya, G., Szatl?zki, G., Vincze, V., K?p?i, E., P??ki, K?m?, J.: Besz?felismer? alap?elj?? az enyhe kognit? zavar automatikus felismer??e spont? besz? alapj?, ?ltal?os Nyelv?zeti Tanulm?yok XXIX. Szerk.: Kenesei I.-B?r?i Z., Akad?iai Kiad? Budapest, pp. 385-405

105, Kovacs, Gy., T?h, L.,Van COmpernolle, D., Ganapathy, S.: Increasing the robustness of CNN acoustic models using ARMA spectrogram features and channel dropout, Pattern Recognition Letters, Vo. 100, pp. 44-50, 2017.

104, Gosztolya, G., Busa-Fekete, R., Gr?z, T., T?h, L.: DNN-based Feature Extraction and Classifier Combination for Child-Directed Speech, Cold and Snoring Identification, Proc. Interspeech 2017.

103, Csap? T.G., Gr?z, T., Gosztolya, G., T?h, L., Mark?A.: DNN-based Ultrasound-to-Speech Conversion for a Silent Speech Interface, Proc. Interspeech 2017.

102, Gr?z, T., Gosztolya, G., T?h, L.: A Comparative Evaluation of GMM-Free State Tying Methods for ASR, Proc. Interspeech 2017.

101, Gr?z, T., Gosztolya, G., T?h, L.: Training Context-Dependent DNN Acoustic Models using Probabilistic Sampling, Proc. Interspeech 2017.

100, Gosztolya, G., T?h, L.: DNN-based Feature Extraction for Conflict Intensity Estimation from Speech, IEEE Signal Processing Letters, Vol. 24, Issue 12, pp. 1837-1841, 2017.

99, T?h, L.: Multi-Resolution Spectral Input for Convolutional Neural Network-based Speech Recognition, Proc. SpeD 2017, pp. 1-6.

98, Bodn?, P., Gr?z, T., T?h, L., Ny?, L.G.: Efficient visual code localization with neural networks, Pattern Analysis and Applications, Vo. 21, No. 1, pp. 249-260, 2018.

97, Gosztolya, G., T?h, L.: A feature selection-based speaker clustering method for paralinguistic tasks, Pattern Analysis and Applications, Vol. 21, No.1, pp. 193-204, 2018.

96, Csap? T.G., Gr?z, T., T?h, L., Mark? A.: Besz?szint?is ultrahangos artikul?i? felv?elekb? m?y neuronh?? seg?s??el, Proc. MSZNY, pp. 181-192, 2017.

95, Gr?z, T., Gosztolya, G., T?h, L.: M?y neuronh?? besz?felismer? GMM-mentes tan??a, Proc. MSZNY, pp. 170-180, 2017.

94, Kov?s, Gy., T?h, L.: M?y neuronh??a integr?t spektro-tempor?is jellemz?inyer?i m?szer optimaliz??a, Proc. MSZNY, pp. 158-169, 2017.

93, Gosztolya, G., Gr?z, T., T?h, L., Beke, A., Neuberger, T.: Neur?is h?? tan??a val?z???i mintav?elez?sel nevet?ek felismer??e, Proc. MSZNY, pp. 136-145, 2017.

92, Gosztolya, G., Beke, A., Neuberger, T., T?h. L.: Laughter Classification using Deep Rectifier Neural Networks with a Minimal Feature Subset, Archives of Acoustics, Vol. 41, No. 4, pp. 669-682, 2016.

91, Gosztolya, G., Gr?z, T., T?h. L.: GMM-Free Flat Start Sequence-Discriminative DNN Training, Interspeech 2016, pp. 3409-3413.

90, Gosztolya, G., T?h, L., Gr?z, T., Vincze, V., Hoffmann, I., Szatl?zki, G., P??ki, M., K?m?, J.: Detecting Mild Cognitive Impairment from Spontaneous Speech by Correlation-Based Phonetic Feature Selection, Interspeech 2016, pp. 107-111.

89, Gosztolya, G., Gr?z, T., Busa-Fekete, R., T?h. L.: Determining Native Language and Deception Using Phonetic Features and Classifier Combination, Interspeech 2016, pp. 2418-2422.

88, Gosztolya, G., Gr?z, T., Szasz?, Gy., T?h. L.: Estimating the Sincerity of Apologies in Speech by DNN Rank Learning and Prosodic Analysis, Interspeech 2016, pp. 2026-2030.

87, T?h, L., Gosztolya, G.: Adaptation of DNN Acoustic Models using KL-divergence Regularization and Multi-task Training, SPECOM 2016, pp. 108-115.

86, Vincze, V., Gosztolya, G., T?h, L., Hoffmann, I., Szatl?zki, G., B?r?i, Z., P??ki, J., K?m?, J.: Detecting Mild Cognitive Impairment by Exploiting Linguistic Information from Transcripts, Proc. ACL 2016, pp. 181-187.

85, Kov?s, Gy., T?h, L.: T?bs?os, zajt??besz?felismer? m?y neuronh??al, Proc. MSZNY 2016, pp. 287-294.

84, T?h, L., Gosztolya, G.: M?y neuronh?? akusztikus modellek gyors adapt?i?a multi-taszk tan??sal, Proc. MSZNY 2016, pp. 154-162.

83, Gosztolya, G., Vincze, V., Gr?z, T., T?h, L.: Egy magyar nyelv?besz?felismer?rendszer sz?zint?hib?nak elemz?e, Proc. MSZNY 2016, pp. 100-110.

82, T?h, L., Gosztolya, G., Vincze, V., Hoffmann, I., Szatl?zki, G., Bir? E., Zsura, F., P??ki, M., K?m?, J.: Az enyhe kognit? zavar automatikus azonos??a besz?felismer?i technik? haszn?at?al, Proc. XVIII. Neumann Kollokvium 2015, pp. 112-117

81, Toth, L.: Phone Recognition with Hierarchical Convolutional Deep Maxout Networks, , EURASIP Journal on Audio, Speech, and Music Processing, 2015:25, 2015.

80, Kov?s Gy., T?h L., : Joint Optimization of Spectro-Temporal Features and Deep Neural Nets for Robust Automatic Speech Recognition, Acta Cybernetica, Volume 22, Number 1 , Page 117-134, 2015.

79, Gr?z, T., Busa-Fekete, R., Gosztolya, G., T?h, L.: Assessing the Degree of Nativeness and Parkinson's Condition Using Gaussian Processes and Deep Rectifier Neural Networks, Proc. Interspeech 2015, pp. 919-923

78, T?h, L., Gosztolya, G., Vincze, V., Hoffmann, I., Szatl?zki, G., Bir? E., Zsura, F., P??ki, M., K?m?, J.: Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech using ASR, Proc. Interspeech 2015, pp. 2694-2698

77, Gosztolya, G., Gr?z, T., T?h, L., Imseng, D.: Building Context-Dependent DNN Acoustic Models using Kullback-Leibler Divergence-Based State Tying, Proc. ICASSP 2015, pp. 4570-4574

76, Toth, L.: Modeling Long Temporal Contexts in Convolutional Neural Network-Based Phone Recognition, Proc. ICASSP 2015, pp. 4575-4579

75, Vincze, V., Hoffmann, I., Szatl?zki, G., B?? E., Gosztolya, G., T?h, L., P??ki, M., K?m?, J.: Az enyhe kognit? zavar automatikus azonos??a besz??iratok alapj?, Proc. MSZNY 2015, pp. 249-256.

74, Gr?z, T., Gosztolya, G., T?h, L.: K?nyezetf?g?akusztikai modellek l?rehoz?a Kullback-Leibler-divergencia alap?klaszterez?sel, Proc. MSZNY 2015, pp. 174-181.

73, Kov?s Gy., T?h L., Van Compernolle, D.: Selection and enhancement of Gabor filters for automatic speech recognition, International Journal of Speech Technology, Volume 18, Issue 1 , Page 1-16, 2015.

72, Bodn? P., Gr?z T., T?h L., Ny? L.: Localization of Visual Codes in the DCT Domain Using Deep Rectifier Neural Networks, Proc. ANNIIP 2014, pp. 37-44.

71, Gr?z T., Bodn? P., T?h L., Ny? L.: QR Code Localization Using Deep Neural Networks, Proc. MLSP 2014, pp. 1-6.

70, Kov?s Gy., T?h L., Gr?z T.: Robust Multi-Band ASR Using Deep Neural Nets and Spectro-temporal Features, Proc. SPECOM 2014, pp. 386-393.

69, Gr?z T., Gosztolya G, T?h L.: A Sequence Training Method for Deep Rectifier Neural Networks in Speech Recognition, Proc. SPECOM 2014, pp. 81-88.

68, Gr?z, T., Gosztolya, G., Busa-Fekete, R., T?h, L.: Detecting the Intensity of Cognitive and Physical Load Using AdaBoost and Deep Rectifier Neural Networks, Proc. Interspeech 2014, pp. 452-456.

67, Toth, L.: Convolutional Deep Maxout Networks for Phone Recognition Proc. Interspeech 2014, pp. 1078-1082.

66, Toth, L.: Combining Time- and Frequency-Domain Convolution in Convolutional Neural Network-Based Phone Recognition Proc. ICASSP 2014, pp. 190-194.

65. Grosz, T., Kovacs, Gy., Toth. L.: Uj eredmenyek a mely neuronhalos magyar nyelvu beszedfelismeresben, Proc. MSZNY, pp. 3-13, 2014.

64. Grosz, T., Toth, L.: A Comparison of Deep Neural Network Training Methods for Large Vocabulary Speech Recognition, Proc. TSD2013, pp. 36-43, 2013.

63. Kovacs, Gy., Toth, L.: The Joint Optimization of Spectro-Temporal Features and Neural Net Classifiers, Proc. TSD2013, pp. 552-559, 2013.

62. Gosztolya, G., Busa-Fekete, R., Toth, L.: Detecting Autism, Emotions and Social Signals Using AdaBoost, Proc. Interspeech 2013, pp. 220-224.

61, Toth, L.: Convolutional Deep Rectifier Neural Nets for Phone Recognition, Proc. Interspeech 2013, pp. 1722-1726.

60, Toth, L.: Phone Recognition with Deep Sparse Rectifier Neural Networks, Proc. ICASSP 2013, pp. 6985-6989.

59, Grosz, T., Toth, L.: Mely neuronhalok az akusztikus modellezesben, Proc. MSZNY 2013, pp. 3-12.

58, Toth, L.: K??letek beszedfelismerok akusztikus modelljenek nyelvek kozotti atvitelere, Altalanos Nyelveszeti Tanulmanyok XXIV, pp. 311-327, 2012.

57, Gosztolya, G., Toth, L.: Improving the Sound Recording Quality of Wireless Sensors Using Automatic Gain Control Methods, Scientific Bulletin of Politechnica University of Timisoara, Vol. 56(70), No. 2, pp. 47-56, June 2011,

56, Toth, L.:A Hierarchical, Context-Dependent Neural Network Architecture for Improved Phone Recognition, Proc. ICASSP 2011, pp. 5040-5043.

55, Kovacs, Gy., Toth, L.: Phone Recognition Experiments with 2D-DCT Spectro-Temporal Features, Proc. SACI 2011, pp. 143-146.

54, Gosztolya, G., Toth, L., Kovacs, Gy.: Spoken Term Detection from Noisy Input, Proc. SACI 2011, pp. 91-96.

53, Gosztolya, G., Toth, L.: Spoken Term Detection Based on the Most Probable Phoneme Sequence, Proc. SAMI 2011, pp. 101-106.

52, Gosztolya, G., Toth, L.:Kulcsszokeresesi kiserletek hangzo hiranyagokon beszedhang alapu felismeresi technologiakkal, Proc. MSZNY 2010, pp. 224-235.

51, Toth L., Tarjan B., Sarosi G., Mihajlik P.: Speech Recognition Experiments with Audiobooks, Acta Cybernetica 19 (2010). pp. 695-713.

50, Gosztolya, G., Paczolay, D., Toth, L.: Low-Complexity Audio Compression Methods For Wireless Sensors,Proc. SISY 2010, pp. 77-81.

49, Gosztolya, G., Paczolay, D., Toth, L.: Automatic Gain Control Algorithms for Wireless Sensors, Proc. ICCC-CONTI 2010, pp. 401-406.

48, Kovacs, Gy., Toth, L.: Localized Spectro-Temporal Features for Noise-Robust Speech Recognition, Proc. ICCC-CONTI 2010, pp. 481-485.

47, Toth, L.: Beszedfelismeresi kiserletek hangoskonyvekkel, Proc. MSZNY, pp. 206-216, 2009.

46, Gosztolya, G., Banhalmi, A., Toth, L.: Using One-Class Classification Techniques in the Anti-Phoneme Problem, Proceedings of IbPRIA 2009 (2009), pp. 433-440.

45, Banhalmi, A., Paczolay, D., Toth, L., Kocsor, A.: Investigating the robustness of a Hungarian medical dictationsystem under various conditions, International Journal of Speech Technology, VOLUME 9, ISSUE 3-4 (2008), PAGE 121-131.

44, Toth, L., Frankel, J., Gosztolya, G., King, S.: Cross-lingual Portability of MLP-Based Tandem Features - A Case Study for English and Hungarian, Proceedings of Interspeech 2008, pp. 2695-2698, 2008.

43, Gosztolya, G., Toth, L.: Detection of Phoneme Boundaries Using Spiking Neurons, Proceedings of ICAISC 2008, pp. 782-793, 2008.

42, Banhalmi, A., Paczolay, D., Toth, L.: Dikt??endszer pontoss??ak ? hat?onys??ak vizsg?ata a keres?i t?en alkalmazott v??i technik? f?gv?y?en, Proc. MSZNY 2007, pp. 56-68.

41, Toth, L.: Benchmarking Human Performance on the Acoustic and Linguistic Subtasks of ASR System, Proceedings of Eurospeech 2007, pp. 382-385, 2007.

40, Banhalmi, A., Paczolay, D., Toth, L. and Kocsor, A.: Development of a Hungarian Medical Dictation System, Informatica, Vol. 31, No. 2, pp. 241-246, 2007.

39, Toth, L., Kocsor, A.: A segment-based interpretation of HMM/ANN hybrids, Computer Speech And Language, Vol. 21, pp. 562-578, 2007.

38, Banhalmi, A., Kocsor, A., Kovacs, K., Toth, L.: Fundamental Frequency Estimation by Combinations of Various Methods, Proc. 7th Nordic Signal Processing Symposium NORSIG 2006.

37, Toth, L.: Posterior-Based Speech Models and their Application to Hungarian Speech Recognition, Ph.D. Dissertation, University of Szeged, 2006.

36, Banhalmi, A., Paczolay, D., Toth, L., Kocsor, A.: First Results of a Hungarian Medical Dictation Project, Proc. of IS-LTC 2006, pp. 23-26.

35, Vicsi, K., Kocsor, A., Toth, L., Velkei, Sz., Szaszak, Gy., Teleki, Cs., Banhalmi, A., Paczolay, D.: A Magyar Referencia Beszedadatb?is ? alkalmaz?a orvosi dikt??rendszerek kifejleszt??ez, Proc. MSZNY 2005, pp. 435-438.

34, Rovo, L., Smehak, Gy., Toth, L., Szamoskozi, A., Toth, F., Kiss, J. G., Czigner, J., Jori, J.: A ketoldali hangszalagbenulas "korai" szakaban vegzett hangrestagito mutet kovetkezteben letrejovo elvaltozasok reverzibilitasanak vizsgalata laryngostroboszkopiaval es obejktiv hangelemzessel, Ful-, orr-, gegegyogyaszat, Vol. 51, No. 2., pp. 85-92, 2005.

33, Banhalmi, A., Kovacs, K., Kocsor, A., Toth, L.: Fundamental Frequency Estimation by Least-Squares Harmonic Model Fitting, Proceedings of EuroSpeech 2005, pp. 305-308.

32, Toth, L., Kocsor, A.: Explicit Duration Modelling in HMM/ANN Hybrids, Matousek et al. (eds.): Proceedings of TSD 2005, LNAI 3658, pp. 310-317, Springer, 2005.

31, Toth, L., Kocsor, A.: Training HMM/ANN Hybrid Speech Recognizers by Probabilistic Sampling,Duch et al. (eds): Proceedings of ICANN 2005, LNCS 3696, pp. 597-603, 2005.

30, Toth, L., Kocsor, A., Csirik, J.: On Naive Bayes in Speech Recognition, Int. J. Applied Mathematics and Computer Science, Vol. 15, No. 2, pp. 287-294, 2005.

29, Zsigri, Gy., Kocsor, A., Toth, L., Sejtes, Gy.: Phonetic Level Annotation and Segmentation of Hungarian Speech Databases, Acta Cybernetica, Vol. 16, pp. 659-673, 2004.

28, Toth, L., Kocsor, L., Gosztolya, G.: Telephone Speech Recognition via the Combination of Knowledge Sources in a Segmental Speech Model, Acta Cybernetica, Vol 16, pp. 643-657, 2004.

27, Zsigri, Gy., Toth, L., Kocsor, A. Sejtes, Gy.: Az automata es kezi szegmentalas ejtesvariaciok okozta problemai, Proc. MSZNY 2004.

26, Vicsi, K., Kocsor, A., Teleki, Cs., Toth, L.: Beszedadatbazis irodai szamitogep-felhasznaloi kornyezetben, Proc. MSZNY 2004.

25, Faddi, G., Kocsor, A, Toth, L.: Initializing Directions in Projection Pursuit Learning, Proc. IJCNN'2004, Paper No. 1429

24, Toth, L., Gosztolya, G.: Replicator Neural Networks for Outlier Modeling in Segmental Speech Recognition, In: Yin, Fuliang; Wang, Jun; Guo, Chengan (Eds.): Advances in Neural Networks - ISNN 2004,Proceedings of International Symposium on Neural Networks, Dalian, China, August 19-21, 2004, Part I, Springer LNCS Vol. 3173, pp. 996-1001.

23, Kocsor, A., Toth, L., Kernel-Based Feature Extraction with a Speech Technology Application, IEEE Trans. Signal Processing, Vol. 52, No. 8, pp. 2250-2263, Aug. 2004.

22, Kocsor, A. and Toth, L.: Application of Kernel-Based Feature Space Transformations and Learning Methods to Phoneme Classification, Applied Intelligence, Vol. 21., No. 2., pp. 129-142, 2004.

21, Toth, L., Kocsor, A., Kovacs, K., Felfoldi, L.: Nyelveszeti tudasforrasok integralasi lehetosegei diszkriminativ szegmens-alapu beszedfelismero rendszerekbe, Proc. MSZNY 2003, pp. 169-174.

20, Toth, L., Kocsor, A., Az MTBA magyar telefonbeszed-adatbazis kezi feldolgozasanak tapasztalatai, A Beszedkutatas 2003 kotetben (szerk. Gosy Maria), pp. 134-146, 2003.

19, Toth, L., Kocsor, A., Harmonic Alternatives to Sine-Wave Speech, Proc. Eurospeech, Geneva, 2003, pp. 2073-2076.

18, Felfoldi, L., Kocsor, A., Toth, L.: Classifier Combination in Speech Recognition, Periodica Polytechnica Ser. El. Eng., Vol. 47, No. 1-2., pp. 125-140, 2003.

17, Gosztolya, G., Kocsor, A., Toth, L., Felfoldi, L.: Various Robust Search Methods in a Hungarian Speech Recognition System, Acta Cybernetica, Vol. 16, No. 2., pp. 229-240, 2003.

16, Paczolay. D., Kocsor, A., Toth, L.: Real-Time Vocal Tract Length Normalization in a Phonological Awareness Teaching System, Matousek, V., Mautner, . (eds.): Proc. of TSD'2003, pp. 309-314.

15, Paczolay, D., Toth, L., Kocsor, A., Kerekes, J.: Gepi tanulas alkalmazasa egy fonologiai tudatossag-fejleszto rendszerben, Alkalmazott Nyelvtudomany, II. evf. 2. szam, 2002, pp. 55-67

14, Kovacs, K., Kocsor, A., Toth, L.: Hungarian Speech Synthesis Using a Phase Exact HNM Approach, in: Grosky, W. I., Plasil, F. (eds.):Proc. of SOFSEM 2002: Theory and Practice of Informatics, Milovy, CzechRepublic, Nov. 2002, pp. 181-185

13, Vicsi, K., Toth, L., Kocsor, A., Gordos, G., Csirik, J.: MTBA -magyar nyelvu telefonbeszed-adatbazis, Hiradastechnika, Vol. LVII, NO.8,pp. 35-43, 2002

12, Kocsor, a., Toth, L., Paczolay, D.: A Nonlinearized DiscriminantAnalysis and its Application to Speech Impediment Therapy, in:V. Matousek, P. Mautner, R. Moucek, K. Tauser (eds): Proceedings of the4th Int. Conf. on Text, Speech and Dialogue, LNAI 2166, pp. 249-257,Springer Verlag, 2001

11, Kocsor, A., Toth, L., Felfoldi, L.: Application of FeatureTransformation and Learning Methods in Phoneme Classification, in:L. Monostori, J. Vancza and M. Ali (Eds.): Proc. 14th Int. Conf. onIndustrial and Engineering Applications of Artificial Intelligenceand Expert Systems, IEA/AIE 2001, LNAI 2070, pp. 502-512, 2001,Springer Verlag

10, Kocsor, A., Kuba, A. Jr., Toth, L.: Phoneme Classification UsingKernel Principal Component Analysis, Periodica Polytechnica, Vol. 44, No.1, pp. 77-90, 2000

09, Toth, L., Kocsor, A., Kovacs, K.: A Discriminative Segmental SpeechModel and its Application to Hungarian Number Recognition,in: P. Sojka, I kopecek, K. Pala (eds.): TSD'2000, LNAI 1902,pp. 307-313, Springer Verlag, 2000

08, Kocsor, A., Toth, L., Kuba, A. Jr., Kovacs, K., Jelasity, M.,Gyimothy, T., Csirik, J.: A Comparative Study of Several Feature SpaceTransformation and Learning Methods for Phoneme Classification,International Journal of Speech Technology, Vol. 3, Number 3/4,2000, pp. 263-276

07, Kocsor, A., Kuba A. Jr., Toth L.: An Overview of the OASIS speech recognition project, Proceedings of the 4th International Conference on Applied Informatics, Aug. 30-Sept. 3, Eger-Noszvaj, Hungary, 1999, pp. 94-102

06, Kocsor, A., Kuba A. Jr., Toth L., Jelasity M., Felfoldi L., Gyimothy T., Csirik J.,: A Segment-Based Statistical Speech Recognition System for Isolated/Continuous Number Recognition, Proceedings of the FUSST'99, Aug. 19-21, Sagadi,Estonia, pp. 201-211

05, Kocsor, A., T?h, L., B?int I.,: On the Optimal Parameters of a Sinusoidal Representation of Signals, Acta Cybernetica 14, pp. 315-330, 1999

04, T?h, L.: Automatic Speech Recognition, (a chapter in the book "Artificial Intelligence", edited by: Fut?Iv?), 1999, Aula, pp. 815-872 (in Hungarian)

03, T?h, L.: Computer, Can You Hear Me?; ? Alaplap magazin, Vol. XVI. No. 6., p. 5-7. , June, 1998 (in Hungarian)

02, T?h, L.: Speech Recognition Based On Phonetic Features, Proceedings of the High Speed Networking International Workshop (Speech Processing Section), 1997, Balatonf?ed, Hungary, pp. 37-40

01, Alexin, Z., Csirik, J., Gyim?hy, T., Jelasity, M., T?h, L.: Learning Phonetic Rules in a Speech Recognition System, Proceedings of ILP'97, Seventh International Workshop on Inductive Logic Programming, 1997, Prag, Czech Republic, LNAI Vol 1297, pp. 37-44 (1997), Springer Verlag