| Multisource i-vectors domain adaptation using maximum mean discrepancy based autoencoders W Lin, MW Mak, JT Chien IEEE/ACM Transactions on Audio, Speech, and Language Processing 26 (12 …, 2018 | 61 | 2018 |
| Reducing domain mismatch by maximum mean discrepancy based autoencoders. WW Lin, MW Mak, L Li, JT Chien Odyssey 23, 162-167, 2018 | 45 | 2018 |
| Multi-level deep neural network adaptation for speaker verification using MMD and consistency regularization W Lin, MM Mak, N Li, D Su, D Yu ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 42 | 2020 |
| Wav2Spk: A simple DNN architecture for learning speaker embeddings from waveforms W Lin, MW Mak International Speech Communication Association (ISCA), 2020 | 41 | 2020 |
| A survey on text-dependent and text-independent speaker verification Y Tu, W Lin, MW Mak IEEE Access 10, 99038-99049, 2022 | 29 | 2022 |
| The I4U mega fusion and collaboration for NIST speaker recognition evaluation 2016 KA Lee, V Hautamäki, T Kinnunen, A Larcher, C Zhang, A Nautsch, ... Interspeech, 1328-1332, 2017 | 26 | 2017 |
| Learning mixture representation for deep speaker embedding using attention W Lin, MW Mak, L Yi International Speech Communication Association (ISCA), 2020 | 17 | 2020 |
| A framework for adapting DNN speaker embedding across languages W Lin, MW Mak, N Li, D Su, D Yu IEEE/ACM Transactions on Audio, Speech, and Language Processing 28, 2810-2822, 2020 | 16 | 2020 |
| Robust speaker verification using population-based data augmentation W Lin, MW Mak ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 13 | 2022 |
| Mixture representation learning for deep speaker embedding W Lin, MW Mak IEEE/ACM Transactions on Audio, Speech, and Language Processing 30, 968-978, 2022 | 12 | 2022 |
| Fast scoring for PLDA with uncertainty propagation via i-vector grouping W Lin, MW Mak, JT Chien Computer Speech & Language 45, 503-515, 2017 | 12 | 2017 |
| UNet-DenseNet for Robust Far-Field Speaker Verification. Z Gao, MW Mak, W Lin INTERSPEECH, 3714-3718, 2022 | 11 | 2022 |
| Robust speaker verification using deep weight space ensemble W Lin, MW Mak IEEE/ACM Transactions on Audio, Speech, and Language Processing 31, 802-812, 2023 | 9 | 2023 |
| Semi-supervised nuisance-attribute networks for domain adaptation W Lin, MW Mak, Y Tu, JT Chien ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 8 | 2019 |
| Discriminative subspace modeling of SNR and duration variabilities for robust speaker verification N Li, MW Mak, WW Lin, JT Chien Computer Speech & Language 45, 83-103, 2017 | 7 | 2017 |
| Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations W Lin, C He, MW Mak, Y Tu International Conference on Machine Learning 2023, 2023 | 5 | 2023 |
| Model-agnostic meta-learning for fast text-dependent speaker embedding adaptation W Lin, MW Mak IEEE/ACM Transactions on Audio, Speech, and Language Processing 31, 1866-1876, 2023 | 5 | 2023 |
| Strategies for end-to-end text-independent speaker verification W Lin, MW Mak, JT Chien International Speech Communication Association (ISCA), 2020 | 4 | 2020 |
| Fast Scoring for PLDA with Uncertainty Propagation. WW Lin, MW Mak Odyssey, 31-38, 2016 | 4 | 2016 |
| Continuous Autoregressive Modeling with Stochastic Monotonic Alignment for Speech Synthesis W Lin, C He arXiv preprint arXiv:2502.01084, 2025 | | 2025 |