Attention-Based Multi-Hypothesis Fusion for Speech Summarization
Author:
Affiliation:
1. NTT Corporation,Japan
2. Language Technologies Institute, Carnegie Mellon University,Pittsburgh,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9687821/9687855/09687977.pdf?arnumber=9687977
Reference43 articles.
1. Attention is all you need;vaswani;Proc NIPS,0
2. A Hierarchical Neural Summarization Framework for Spoken Documents
3. A method for translation of paralinguistic information;kano;Proc IWSLT,0
4. Why word error rate is not a good metric for speech recognizer training for the speech translation task?
5. Overview of the iwslt 2010 evaluation campaign;paul;Proc IWSLT,0
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2. Espnet-Summ: Introducing a Novel Large Dataset, Toolkit, and a Cross-Corpora Evaluation of Speech Summarization Systems;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16
3. Automatic Speech Recognition with BERT and CTC Transformers: A Review;2023 2nd International Conference on Electronics, Energy and Measurement (IC2EM);2023-11-28
4. Speech Summarization of Long Spoken Document: Improving Memory Efficiency of Speech/Text Encoders;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04
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