| Layer-wise Analysis of a Self-supervised Speech Representation Model A Pasad, JC Chou, K Livescu IEEE Automatic Speech Recognition and Understanding Workshop 2021, 2021 | 330 | 2021 |
| Comparative layer-wise analysis of self-supervised speech models A Pasad, B Shi, K Livescu ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 117 | 2023 |
| Slue: New benchmark tasks for spoken language understanding evaluation on natural speech S Shon, A Pasad, F Wu, P Brusco, Y Artzi, K Livescu, KJ Han ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 81 | 2022 |
| SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks S Shon, S Arora, CJ Lin, A Pasad, F Wu, R Sharma, WL Wu, HY Lee, ... ACL, 2023 | 34 | 2023 |
| Taskology: Utilizing task relations at scale Y Lu, S Pirk, J Dlabal, A Brohan, A Pasad, Z Chen, V Casser, A Angelova, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 31 | 2021 |
| What do self-supervised speech models know about words? A Pasad, CM Chien, S Settle, K Livescu Transactions of the Association for Computational Linguistics 12, 372-391, 2024 | 29 | 2024 |
| On the use of external data for spoken named entity recognition A Pasad, F Wu, S Shon, K Livescu, KJ Han NAACL, 2022 | 22 | 2022 |
| Hidden state variability of pretrained language models can guide computation reduction for transfer learning S Xie, J Qiu, A Pasad, L Du, Q Qu, H Mei Findings of EMNLP, 2022 | 21 | 2022 |
| Automatic Assessment of Reading with Speech RecognitionTechnology RAO Preeti, P Swarup, A Pasad, H Tulsiani, GG Das International Conference on Computers in Education, 2016 | 21 | 2016 |
| Self-supervised speech representations are more phonetic than semantic K Choi, A Pasad, T Nakamura, S Fukayama, K Livescu, S Watanabe arXiv preprint arXiv:2406.08619, 2024 | 13 | 2024 |
| On the contributions of visual and textual supervision in low-resource semantic speech retrieval A Pasad, B Shi, H Kamper, K Livescu Interspeech, 2019, 2019 | 13 | 2019 |
| Dynamic-superb phase-2: A collaboratively expanding benchmark for measuring the capabilities of spoken language models with 180 tasks C Huang, WC Chen, S Yang, AT Liu, CA Li, YX Lin, WC Tseng, A Diwan, ... arXiv preprint arXiv:2411.05361, 2024 | 6 | 2024 |
| On the evaluation of speech foundation models for spoken language understanding S Arora, A Pasad, CM Chien, J Han, R Sharma, J Jung, H Dhamyal, ... arXiv preprint arXiv:2406.10083, 2024 | 6 | 2024 |
| Voice activity detection for children's read speech recognition in noisy conditions A Pasad, K Sabu, P Rao 2017 Twenty-third National Conference on Communications (NCC), 1-6, 2017 | 5 | 2017 |
| Improving Semantic Segmentation through Spatio-Temporal Consistency Learned from Videos A Pasad, A Gordon, TY Lin, A Angelova Learning from Unlabeled Videos, CVPR Workshop, 2020, 2020 | 2 | 2020 |
| Layer-wise analysis of a self-supervised speech representation A Pasad, JC Chou, K Livescu Workshop on Machine Learning in Speech and Language Processing 2021, 2021 | 1* | 2021 |
| Training and Inference Efficiency of Encoder-Decoder Speech Models P Żelasko, K Dhawan, D Galvez, KC Puvvada, A Pasad, NR Koluguri, ... arXiv preprint arXiv:2503.05931, 2025 | | 2025 |
| Taskology: Utilizing Task Relations at Scale (Supplemental Material) Y Lu, S Pirk, J Dlabal, A Brohan, A Pasad, Z Chen, V Casser, A Angelova, ... | | |
Karen LivescuTTI-ChicagoVerified email at ttic.edu
