| 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 | 51 | 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 | 37 | 2020 |
| Reducing domain mismatch by maximum mean discrepancy based autoencoders. WW Lin, MW Mak, L Li, JT Chien Odyssey 23, 162-167, 2018 | 37 | 2018 |
| Wav2Spk: A Simple DNN Architecture for Learning Speaker Embeddings from Waveforms. WW Lin, MW Mak Interspeech, 3211-3215, 2020 | 35 | 2020 |
| 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 | 27 | 2017 |
| Learning Mixture Representation for Deep Speaker Embedding Using Attention. W Lin, MW Mak, L Yi Odyssey, 210-214, 2020 | 18 | 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 | 15 | 2020 |
| A survey on text-dependent and text-independent speaker verification Y Tu, W Lin, MW Mak IEEE Access 10, 99038-99049, 2022 | 14 | 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 | 13 | 2017 |
| Robust speaker verification using population-based data augmentation W Lin, MW Mak ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 11 | 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 | 10 | 2022 |
| 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 |
| 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 | 7 | 2023 |
| UNet-DenseNet for Robust Far-Field Speaker Verification. Z Gao, MW Mak, W Lin INTERSPEECH, 3714-3718, 2022 | 7 | 2022 |
| 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 |
| Strategies for End-to-End Text-Independent Speaker Verification. W Lin, MW Mak, JT Chien Interspeech, 4308-4312, 2020 | 4 | 2020 |
| Fast Scoring for PLDA with Uncertainty Propagation. WW Lin, MW Mak Odyssey, 31-38, 2016 | 4 | 2016 |
| 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 | 2 | 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, 2023 | 1 | 2023 |
| Asymmetric Clean Segments-Guided Self-Supervised Learning for Robust Speaker Verification CX Gan, MW Mak, W Lin, JT Chien ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | | 2024 |
