Combining multiple end-to-end speech recognition models based on density ratio approach-Reference-Cited by-同舟云学术

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Combining multiple end-to-end speech recognition models based on density ratio approach

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:

Hojo Keigo¹,Mori Daiki¹,Wakabayashi Yukoh¹,Ohta Kengo²,Ogawa Atsunori³,Kitaoka Norihide¹

Affiliation:

1. Toyohashi University of Technology,Japan

2. National Insitute of Technology, Anan College,Japan

3. Nippon Telegraph and Telephone Corporation,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10317071/10317095/10317388.pdf?arnumber=10317388

Reference29 articles.

1. On using monolingual corpora in neural machine translation;gulcehre;Proc WMT,2018

2. An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model

3. Component Fusion: Learning Replaceable Language Model Component for End-to-end Speech Recognition System

4. Cold Fusion: Training Seq2Seq Models Together with Language Models

5. Multi-level language modeling and decoding for open vocabulary end-to-end speech recognition

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