Postdoctoral Scholar  |  Zezario, Ryandhimas Edo       Publications   Journal Articles   1. R. E. Zezario, S. -W. Fu, F. Chen, C. -S. Fuh, H. -M. Wang and Y. Tsao, "Deep Learning-Based Non-Intrusive Multi-Objective Speech Assessment Model With Cross-Domain Features," IEEE/ACM Transactions on Audio, Speech, and Language Processing, volume 31, pages 54-70, September 2022. 2. C. Yu*, R. E. Zezario*, S.-S. Wang, J. Sherman, Y.-Y. Hsieh, X. Lu, H.-M. Wang, and Y. Tsao, "Speech Enhancement based on Denoising Autoencoder with Multi-branched Encoders," IEEE/ACM Transactions on Audio, Speech, and Language Processing, volume 28, pages 2756-2769, October 2020, (*equal contributions)     Conference Papers   1. R. E. Zezario, Y.-W. Chen, S.-W. Fu, Y. Tsao, H.-M. Wang, C.-S. Fuh, "A Study on Incorporating Whisper for Robust Speech Assessment," IEEE ICME 2024, July 2024, (Top Performance on the Track 3 - VoiceMOS Challenge 2023) 2. R. E. Zezario, Bo-Ren Brian Bai, Chiou-Shann Fuh, Hsin-Min Wang, Yu Tsao, "Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model," IEEE ICASSP 2024, April 2024. 3. R. E. Zezario, S.-W. Fu, F. Chen, C.-S. Fuh, H.-M. Wang and Y. Tsao, "MTI-Net: A Multi-Target Speech Intelligibility Prediction Model," Interspeech 2022, pages 5463-5467, September 2022. 4. R. E. Zezario, F. Chen, C.-S. Fuh, H.-M. Wang and Y. Tsao, "MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids," Interspeech 2022, pages 3944-3948, September 2022, 1st Place, Machine Learning Challenges for Hearing Aids Challenge; 1st Place, The Hearing Industry Research Consortium Student Prize 5. R. E. Zezario, C. -S. Fuh, H. -M. Wang and Y. Tsao,, "Speech Enhancement with Zero-Shot Model Selection," 2021 29th European Signal Processing Conference (EUSIPCO, December 2021. 6. R. E. Zezario, S. -W. Fu, C. -S. Fuh, Y. Tsao and H. -M. Wang, "STOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model," 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), number 482-486, December 2020. 7. R. E. Zezario, T. Hussain, X. Lu, H. -M. Wang and Y. Tsao, "Self-Supervised Denoising Autoencoder with Linear Regression Decoder for Speech Enhancement," 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6669-6673, May 2020. 8. R. E. Zezario, J. W. C. Sigalingging, T. Hussain, J. -C. Wang and Y. Tsao, "Comparative Study of Masking and Mapping Based on Hierarchical Extreme Learning Machine for Speech Enhancement,," 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), December 2019. 9. R. E. Zezario,S.-W. Fu, X. Lu, H.-M. Wang, and Y. Tsao, "Specialized Speech Enhancement Model Selection Based on Learned Non-Intrusive Quality Assessment Metric," Interspeech 2019, pages 3168- 3172, September 2019. 10. R. E. Zezario, J. Huang, X. Lu, Y. Tsao, H. Hwang and H. Wang,, "Deep Denoising Autoencoder Based Post Filtering for Speech Enhancement," 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pages 373-377, November 2018. 11. C. -Y. Hsu, R. E. Zezario, J. -C. Wang, C. -W. Ho, X. Lu and Y. Tsao, "Incorporating local environment information with ensemble neural networks to robust automatic speech recognition," 2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP), October 2016.