{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:17:12Z","timestamp":1743031032921,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819743988"},{"type":"electronic","value":"9789819743995"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-4399-5_7","type":"book-chapter","created":{"date-parts":[[2024,7,6]],"date-time":"2024-07-06T16:01:52Z","timestamp":1720281712000},"page":"69-79","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Universal Transformer: Block Reusing with\u00a0Adaptor in\u00a0Transformer for\u00a0Automatic Speech Recognition"],"prefix":"10.1007","author":[{"given":"Haoyu","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoyi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinfeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,7]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Bu, H., Du, J., Na, X., Wu, B., Zheng, H.: AISHELL-1: an open-source mandarin speech corpus and a speech recognition baseline. In: 2017 20th Conference of the Oriental Chapter of the International Coordinating Committee on Speech Databases and Speech I\/O Systems and Assessment (O-COCOSDA), pp. 1\u20135. IEEE (2017)","DOI":"10.1109\/ICSDA.2017.8384449"},{"key":"7_CR2","unstructured":"Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., Kaiser, \u0141.: Universal transformers. arXiv preprint arXiv:1807.03819 (2018)"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Dong, L., Xu, S., Xu, B.: Speech-transformer: a no-recurrence sequence-to-sequence model for speech recognition. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5884\u20135888. IEEE (2018)","DOI":"10.1109\/ICASSP.2018.8462506"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Gao, Z., Yao, Y., Zhang, S., Yang, J., Lei, M., McLoughlin, I.: Extremely low footprint end-to-end ASR system for smart device. arXiv preprint arXiv:2104.05784 (2021)","DOI":"10.21437\/Interspeech.2021-819"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Graves, A., Fern\u00e1ndez, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 369\u2013376 (2006)","DOI":"10.1145\/1143844.1143891"},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Gulati, A., et al.: Conformer: convolution-augmented transformer for speech recognition. arXiv preprint arXiv:2005.08100 (2020)","DOI":"10.21437\/Interspeech.2020-3015"},{"key":"7_CR7","unstructured":"Houlsby, N., et al.: Parameter-efficient transfer learning for NLP. In: International Conference on Machine Learning, pp. 2790\u20132799. PMLR (2019)"},{"key":"7_CR8","unstructured":"Kornblith, S., Norouzi, M., Lee, H., Hinton, G.: Similarity of neural network representations revisited. In: International Conference on Machine Learning, pp. 3519\u20133529. PMLR (2019)"},{"key":"7_CR9","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: ALBERT: a lite BERT for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019)"},{"key":"7_CR10","unstructured":"Nakatani, T.: Improving transformer-based end-to-end speech recognition with connectionist temporal classification and language model integration. In: Proceeding Interspeech (2019)"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Park, D.S., et al.: SpecAugment: a simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779 (2019)","DOI":"10.21437\/Interspeech.2019-2680"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Park, D.S., et al.: SpecAugment on large scale datasets. In: 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2020, pp. 6879\u20136883. IEEE (2020)","DOI":"10.1109\/ICASSP40776.2020.9053205"},{"issue":"140","key":"7_CR13","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1\u201367 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Shao, Y., Wang, Y., Povey, D., Khudanpur, S.: PyChain: a fully parallelized PyTorch implementation of LF-MMI for end-to-end ASR. arXiv preprint arXiv:2005.09824 (2020)","DOI":"10.21437\/Interspeech.2020-3053"},{"issue":"8","key":"7_CR15","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/JSTSP.2017.2763455","volume":"11","author":"S Watanabe","year":"2017","unstructured":"Watanabe, S., Hori, T., Kim, S., Hershey, J.R., Hayashi, T.: Hybrid CTC\/attention architecture for end-to-end speech recognition. IEEE J. Sel. Top. Sign. Process. 11(8), 1240\u20131253 (2017)","journal-title":"IEEE J. Sel. Top. Sign. Process."},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Winata, G.I., Cahyawijaya, S., Lin, Z., Liu, Z., Fung, P.: Lightweight and efficient end-to-end speech recognition using low-rank transformer. In: 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2020, pp. 6144\u20136148. IEEE (2020)","DOI":"10.1109\/ICASSP40776.2020.9053878"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, Q., Woodland, P.C.: Knowledge distillation for neural transducers from large self-supervised pre-trained models. In: 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2022, pp. 8527\u20138531. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9746168"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Zhao, S., Pascual, D., Brunner, G., Wattenhofer, R.: Of non-linearity and commutativity in BERT. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9533563"}],"container-title":["Lecture Notes in Computer Science","Advances in Neural Networks \u2013 ISNN 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-4399-5_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,6]],"date-time":"2024-07-06T16:02:58Z","timestamp":1720281778000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-4399-5_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819743988","9789819743995"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-4399-5_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"7 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISNN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Weihai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isnn2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conference.cs.cityu.edu.hk\/isnn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}