Flexible Evidence Model to Reduce Uncertainty Mismatch Between Speech Enhancement and ASR Based on Encoder-Decoder Architecture-Reference-Cited by-同舟云学术

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Flexible Evidence Model to Reduce Uncertainty Mismatch Between Speech Enhancement and ASR Based on Encoder-Decoder Architecture

Published:2023-10-31 Issue: Volume: Page:
ISSN:
Container-title:2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
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Author:

Takeda Ryu¹,Sudo Yui²,Komatani Kazunori¹

Affiliation:

1. Osaka University,SANKEN,Japan

2. Honda Research Institute Japan, Co., Ltd.,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10317071/10317095/10317247.pdf?arnumber=10317247

Reference33 articles.

1. Uncertainty propagation through deep neural networks

2. Handling derivative filterbank features in bounded-marginalization-based missing data automatic speech recognition

3. An extended experimental investigation of DNN uncertainty propagation for noise robust ASR

4. Uncertainty training and decoding methods of deep neural networks based on stochastic representation of enhanced features

5. On the difficulty of training recurrent neural networks;pascanu;Proc of International Conference on Machine Learning,2013

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improving Noise Robustness of Automatic Speech Recognition Based on a Parallel Adapter Model with Near-Identity Initialization;Lecture Notes in Computer Science;2024

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