| GlottDNN-a full-band glottal vocoder for statistical parametric speech synthesis. M Airaksinen, B Bollepalli, L Juvela, Z Wu, S King, P Alku Interspeech 9, 2473-2477, 2016 | 42 | 2016 |
| Speech waveform synthesis from MFCC sequences with generative adversarial networks L Juvela, B Bollepalli, X Wang, H Kameoka, M Airaksinen, J Yamagishi, ... 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 35 | 2018 |
| HIGH-PITCHED EXCITATION GENERATION FOR GLOTTAL VOCODING IN STATISTICAL PARAMETRIC SPEECH SYNTHESIS USING A DEEP NEURAL NETWORK L Juvela, B Bollepalli, M Airaksinen, P Alku ICASSP 2016, 2016 | 35 | 2016 |
| Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis B Bollepalli, L Juvela, P Alku Interspeech 2017, 3394--3398, 2017 | 33 | 2017 |
| Speaker-independent raw waveform model for glottal excitation L Juvela, V Tsiaras, B Bollepalli, M Airaksinen, J Yamagishi, P Alku arXiv preprint arXiv:1804.09593, 2018 | 32 | 2018 |
| A comparison between straight, glottal, and sinusoidal vocoding in statistical parametric speech synthesis M Airaksinen, L Juvela, B Bollepalli, J Yamagishi, P Alku IEEE/ACM Transactions on Audio, Speech, and Language Processing 26 (9), 1658 …, 2018 | 25 | 2018 |
| Glotnet—a raw waveform model for the glottal excitation in statistical parametric speech synthesis L Juvela, B Bollepalli, V Tsiaras, P Alku IEEE/ACM Transactions on Audio, Speech, and Language Processing 27 (6), 1019 …, 2019 | 17 | 2019 |
| GELP: GAN-Excited linear prediction for speech synthesis from mel-spectrogram L Juvela, B Bollepalli, J Yamagishi, P Alku arXiv preprint arXiv:1904.03976, 2019 | 16 | 2019 |
| Modeling a Noisy-channel for Voice Conversion Using Articulatory Features B Bollepalli, AW Black, K Prahallad INTERSPEECH, 2012 | 13 | 2012 |
| Lombard speech synthesis using long short-term memory recurrent neural networks B Bollepalli, M Airaksinen, P Alku 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 12 | 2017 |
| SWS Task: Articulatory phonetic units and sliding DTW GV Mantena, B Bajibabu, K Prahallad Media Eval, INTERSPEECH, 2011 | 8 | 2011 |
| Waveform generation for text-to-speech synthesis using pitch-synchronous multi-scale generative adversarial networks L Juvela, B Bollepalli, J Yamagishi, P Alku ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 7 | 2019 |
| Tutoring robots S Al Moubayed, J Beskow, B Bollepalli, A Hussen-Abdelaziz, ... International Summer Workshop on Multimodal Interfaces, 80-113, 2013 | 7 | 2013 |
| Duration modelling in voice conversion using artificial neural networks R Srikanth, B Bajibabu, K Prahallad 2012 19th International Conference on Systems, Signals and Image Processing …, 2012 | 7 | 2012 |
| DNN-based Speech Synthesis for Indian Languages from ASCII text S Ronanki, S Reddy, B Bollepalli, S King arXiv preprint arXiv:1608.05374, 2016 | 6 | 2016 |
| A comparative evaluation of vocoding techniques for hmm-based laughter synthesis B Bollepalli, J Urbain, T Raitio, J Gustafson, H Cakmak 2014 IEEE international conference on acoustics, speech and signal …, 2014 | 6 | 2014 |
| Analysis of breathy voice based on excitation characteristics of speech production SA Thati, B Bollepalli, P Bhaskararao, B Yegnanarayana 2012 International Conference on Signal Processing and Communications (SPCOM …, 2012 | 6 | 2012 |
| Normal-to-Lombard adaptation of speech synthesis using long short-term memory recurrent neural networks B Bollepalli, L Juvela, M Airaksinen, C Valentini-Botinhao, P Alku Speech Communication 110, 64-75, 2019 | 5 | 2019 |
| Reducing Mismatch in Training of DNN-Based Glottal Excitation Models in a Statistical Parametric Text-to-Speech System. L Juvela, B Bollepalli, J Yamagishi, P Alku Interspeech, 1368-1372, 2017 | 5 | 2017 |
| Voice conversion using articulatory features B Bollepalli IIIT Hyderabad, 2012 | 5* | 2012 |
Lauri JuvelaDepartment of Signal Processing and Acoustics, Aalto UniversityEmail confirmado em aalto.fi
