CATNet: Cross-modal fusion for audio–visual speech recognition-Reference-Cited by-同舟云学术

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CATNet: Cross-modal fusion for audio–visual speech recognition

Published:2024-02 Issue: Volume:178 Page:216-222
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

Wang Xingmei,Mi Jiachen,Li Boquan,Zhao Yixu,Meng Jiaxiang

Publisher

Elsevier BV

Reference36 articles.

1. End-to-end visual speech recognition for small-scale datasets;Petridis;Pattern Recognit. Lett.,2020

2. An analysis of the effect of combining standard and alternate sensor signals on recognition of syllabic units for multimodal speech recognition;Radha;Pattern Recognit. Lett.,2018

3. Next-generation of virtual personal assistants (microsoft cortana, apple siri, amazon alexa and google home);Kepuska,2018

4. Scope for deep learning: A study in audio-visual speech recognition;Bhaskar,2019

5. L. Deng, J. Platt, Ensemble Deep Learning for Speech Recognition, in: Proc. Interspeech, 2014.

Cited by 3 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. USAT: A Universal Speaker-Adaptive Text-to-Speech Approach;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024

2. Optimizing Audio-Visual Speech Enhancement Using Multi-Level Distortion Measures for Audio-Visual Speech Recognition;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024

3. Multimodal Dual Cross-Attention Fusion Strategy for Autonomous Garbage Classification System;IEEE Transactions on Industrial Informatics;2024

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