Leveraging Sequence-to-Sequence Speech Synthesis for Enhancing Acoustic-to-Word Speech Recognition-Reference-Cited by-同舟云学术

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Leveraging Sequence-to-Sequence Speech Synthesis for Enhancing Acoustic-to-Word Speech Recognition

Published:2018-12 Issue: Volume: Page:
ISSN:
Container-title:2018 IEEE Spoken Language Technology Workshop (SLT)
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Author:

Mimura Masato,Ueno Sei,Inaguma Hirofumi,Sakai Shinsuke,Kawahara Tatsuya

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8632666/8639030/08639589.pdf?arnumber=8639589

Cited by 37 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. Accurate synthesis of dysarthric Speech for ASR data augmentation;Speech Communication;2024-10

2. Exploring the Viability of Sequence-to-Sequence Models for Automatic Speech Recognition in Peer-to-Peer Learning Environments;2024 8th International Conference on Computer, Software and Modeling (ICCSM);2024-07-04

3. Multi-setting acoustic feature training for data augmentation of speech recognition;Acoustical Science and Technology;2024-07-01

4. A deep learning approach to dysarthric utterance classification with BiLSTM-GRU, speech cue filtering, and log mel spectrograms;The Journal of Supercomputing;2024-03-20

5. Leveraging Synthetic Speech for CIF-Based Customized Keyword Spotting;Communications in Computer and Information Science;2024

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