A CTC Alignment-Based Non-Autoregressive Transformer for End-to-End Automatic Speech Recognition
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
2. PAII Inc., Palo Alto, CA, USA
Funder
NSF
UCLA-Amazon Science Hub
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Acoustics and Ultrasonics,Computer Science (miscellaneous),Computational Mathematics
Link
https://ieeexplore.ieee.org/ielam/6570655/9970249/10089490-aam.pdf
Reference59 articles.
1. Non-Autoregressive Transformer for Speech Recognition
2. Improving Non-Autoregressive End-to-End Speech Recognition with Pre-Trained Acoustic and Language Models
3. BANG: Bridging autoregressive and non-autoregressive generation with large scale pretraining;qi;Proc Int Conf Mach Learn,0
4. Towards Better Domain Adaptation for Self-Supervised Models: A Case Study of Child ASR
5. Fast End-to-End Speech Recognition Via Non-Autoregressive Models and Cross-Modal Knowledge Transferring From BERT
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