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ID 115877
Title Alternative
E2E SPEECH RECOGNITION WITH CTC AND LOCAL ATTENTION
Author
Chen, Jiahao Tokushima University
Kitaoka, Norihide Toyohashi University of Technology KAKEN Search Researchers
Keywords
CTC
Local attention
Speech recognition
Streaming recognition
Content Type
Journal Article
Description
Many end-to-end, large vocabulary, continuous speech recognition systems are now able to achieve better speech recognition performance than conventional systems. Most of these approaches are based on bidirectional networks and sequence-to-sequence modeling however, so automatic speech recognition (ASR) systems using such techniques need to wait for an entire segment of voice input to be entered before they can begin processing the data, resulting in a lengthy time-lag, which can be a serious drawback in some applications. An obvious solution to this problem is to develop a speech recognition algorithm capable of processing streaming data. Therefore, in this paper we explore the possibility of a streaming, online, ASR system for Japanese using a model based on unidirectional LSTMs trained using connectionist temporal classification (CTC) criteria, with local attention. Such an approach has not been well investigated for use with Japanese, as most Japanese-language ASR systems employ bidirectional networks. The best result for our proposed system during experimental evaluation was a character error rate of 9.87%.
Journal Title
APSIPA Transactions on Signal and Information Processing
ISSN
20487703
Publisher
Cambridge University Press
Volume
9
Start Page
e25
Published Date
2020-11-23
Rights
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
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language
eng
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departments
Science and Technology