| An unsupervised autoregressive model for speech representation learning YA Chung, WN Hsu, H Tang, J Glass Interspeech, 2019 | 443 | 2019 |
| Multitask learning with low-level auxiliary tasks for encoder-decoder based speech recognition S Toshniwal, H Tang, L Lu, K Livescu Interspeech, 2017 | 127 | 2017 |
| Vector-quantized autoregressive predictive coding YA Chung, H Tang, J Glass Interspeech, 2020 | 114 | 2020 |
| Frame-level speaker embeddings for text-independent speaker recognition and analysis of end-to-end model S Shon, H Tang, J Glass IEEE Workshop on Spoken Language Technology (SLT), 2018 | 99 | 2018 |
| VoiceID loss: Speech enhancement for speaker verification S Shon, H Tang, J Glass Interspeech, 2019 | 91 | 2019 |
| A deep residual network for large-scale acoustic scene analysis L Ford, H Tang, F Grondin, J Glass Interspeech, 2019 | 54 | 2019 |
| Lexicon-free fingerspelling recognition from video: Data, models, and signer adaptation T Kim, J Keane, W Wang, H Tang, J Riggle, G Shakhnarovich, D Brentari, ... Computer Speech & Language, 2017 | 49 | 2017 |
| ASR for under-resourced languages from probabilistic transcription M Hasegawa-Johnson, P Jyothi, D McCloy, M Mirbagheri, G di Liberto, ... IEEE Transactions on Audio, Speech and Language Processing, 2017 | 40 | 2017 |
| End-to-end neural segmental models for speech recognition H Tang, L Lu, L Kong, K Gimpel, K Livescu, C Dyer, NA Smith, S Renals IEEE Journal of Selected Topics in Signal Processing 11 (8), 1254-1264, 2017 | 38 | 2017 |
| Hierarchical sketch induction for paraphrase generation T Hosking, H Tang, M Lapata Annual Meeting of the Association for Computational Linguistics (ACL), 2022 | 33 | 2022 |
| Adapting ASR for under-resourced languages using mismatched transcriptions C Liu, P Jyothi, H Tang, V Manohar, M Hasegawa-Johnson, S Khudanpur IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016 | 31 | 2016 |
| A study of enhancement, augmentation, and autoencoder methods for domain adaptation in distant speech recognition H Tang, WN Hsu, F Grondin, J Glass Interspeech, 2018 | 29 | 2018 |
| Time-contrastive learning based deep bottleneck features for text-dependent speaker verification AK Sarkar, ZH Tan, H Tang, S Shon, J Glass arXiv preprint arXiv:1905.04554, 2019 | 27 | 2019 |
| On training recurrent networks with truncated backpropagation through time in speech recognition H Tang, J Glass IEEE Workshop on Spoken Language Technology (SLT), 2018 | 25 | 2018 |
| Discriminative segmental cascades for feature-rich phone recognition H Tang, W Wang, K Gimpel, K Livescu IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), 561-568, 2015 | 22 | 2015 |
| Unsupervised adaptation with interpretable disentangled representations for distant conversational speech recognition WN Hsu, H Tang, J Glass Interspeech, 2018 | 20 | 2018 |
| Analyzing acoustic word embeddings from pre-trained self-supervised speech models R Sanabria, H Tang, S Goldwater IEEE International Conference on Acoustics, Speech and Signal Processing …, 2023 | 19 | 2023 |
| Signer-independent fingerspelling recognition with deep neural network adaptation T Kim, W Wang, H Tang, K Livescu IEEE International Conference on Acoustics, Speech and Signal Processing …, 2016 | 19 | 2016 |
| Conditioning and sampling in variational diffusion models for speech super-resolution CY Yu, SL Yeh, G Fazekas, H Tang IEEE International Conference on Acoustics, Speech and Signal Processing …, 2023 | 17 | 2023 |
| Phonetic analysis of self-supervised representations of English speech D Wells, H Tang, K Richmond Interspeech, 2022 | 17 | 2022 |
James GlassMIT Computer Science and Artificial Intelligence LaboratoryVerified email at mit.edu
