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András Beke
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2020 – today
- 2021
- [j3]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
- [c21]Mark A. Huckvale, András Beke, Mirei Ikushima:
Prediction of Sleepiness Ratings from Voice by Man and Machine. INTERSPEECH 2020: 4571-4575
2010 – 2019
- 2019
- [c20]Gábor Gosztolya, András Beke, Tilda Neuberger:
Differentiating Laughter Types via HMM/DNN and Probabilistic Sampling. SPECOM 2019: 122-132 - [i1]Dávid Sztahó, György Szaszák, András Beke:
Deep learning methods in speaker recognition: a review. CoRR abs/1911.06615 (2019) - 2018
- [c19]András Beke:
Forensic speaker profiling in a Hungarian speech corpus. CogInfoCom 2018: 379-384 - [c18]Máté Ákos Tündik, György Szaszák, Gábor Gosztolya, András Beke:
User-centric Evaluation of Automatic Punctuation in ASR Closed Captioning. INTERSPEECH 2018: 2628-2632 - 2017
- [c17]Mark A. Huckvale, András Beke:
It Sounds Like You Have a Cold! Testing Voice Features for the Interspeech 2017 Computational Paralinguistics Cold Challenge. INTERSPEECH 2017: 3447-3451 - 2016
- [c16]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 - [c15]András Beke, György Szaszák:
Automatic Summarization of Highly Spontaneous Speech. SPECOM 2016: 140-147 - 2015
- [c14]András Beke, Viktória Horváth:
Hidden Markov Model-based approach for nasalized vowels recognition in spontaneous speech. ICPhS 2015 - [c13]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 - [c12]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
- [j2]András Beke, Mária Gósy:
Phonetic analysis and automatic prediction of vowel duration in Hungarian spontaneous speech. Intell. Decis. Technol. 8(4): 301-314 (2014) - [c11]András Beke, György Szaszák:
Combining NLP techniques and acoustic analysis for semantic focus detection in speech. CogInfoCom 2014: 493-497 - [c10]Tilda Neuberger, Dorottya Gyarmathy, Tekla Etelka Gráczi, Viktória Horváth, Mária Gósy, András Beke:
Development of a Large Spontaneous Speech Database of Agglutinative Hungarian Language. TSD 2014: 424-431 - 2013
- [c9]András Beke, György Szaszák, Viola Varadi
:
Automatic phrase segmentation and clustering in spontaneous speech. CogInfoCom 2013: 459-462 - [c8]György Szaszák, András Beke:
Using phonological phrase segmentation to improve automatic keyword spotting for the highly agglutinating Hungarian language. INTERSPEECH 2013: 1589-1593 - [c7]András Beke, Mária Gósy, Viktória Horváth:
Boundary Markers in Spontaneous Hungarian Speech. LTC 2013: 3-15 - [c6]Tilda Neuberger, András Beke:
Automatic Laughter Detection in Spontaneous Speech Using GMM-SVM Method. TSD 2013: 113-120 - 2012
- [j1]György Szaszák, András Beke:
Exploiting Prosody for Syntactic Analysis in Automatic Speech Understanding. J. Lang. Model. 0(1): 143-172 (2012) - [c5]András Beke, Mária Gósy:
Characteristics and spectral features used in automatic prediction of vowel duration in spontaneous speech. CogInfoCom 2012: 65-70 - [c4]György Szaszák, András Beke:
Automatic prosodic and syntactic analysis from speech in cognitive infocommunication. CogInfoCom 2012: 377-382 - [c3]András Beke, György Szaszák:
Unsupervised Clustering of Prosodic Patterns in Spontaneous Speech. TSD 2012: 648-655 - 2011
- [c2]György Szaszák, Katalin Nagy, András Beke:
Analysing the Correspondence Between Automatic Prosodic Segmentation and Syntactic Structure. INTERSPEECH 2011: 1057-1060 - [c1]Tekla Etelka Gráczi, Steven M. Lulich, Tamás Gábor Csapó, András Beke:
Context and Speaker Dependency in the Relation of Vowel Formants and Subglottal Resonances - Evidence from Hungarian. INTERSPEECH 2011: 1901-1904
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