Folding Attention: Memory and Power Optimization for On-Device Transformer-Based Streaming Speech Recognition-Reference-Cited by-同舟云学术

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Folding Attention: Memory and Power Optimization for On-Device Transformer-Based Streaming Speech Recognition

Published:2024-04-14 Issue: Volume: Page:11901-11905
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Li Yang¹,Lai Liangzhen¹,Shangguan Yuan¹,Iandola Forrest N.¹,Ni Zhaoheng¹,Chang Ernie¹,Shi Yangyang¹,Chandra Vikas¹

Affiliation:

1. Meta AI

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10447827.pdf?arnumber=10447827

Reference28 articles.

1. Attention Is All You Need;Vaswani;NeurIPS,2017

2. Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for Speech Recognition

3. A Comparative Study on Transformer vs RNN in Speech Applications

4. Self-Attentional Acoustic Models

5. Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese

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. Dvsa: A Focused and Efficient Sparse Attention Via Explicit Selection for Speech Recognition;2024

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