Joint bottleneck feature and attention model for speech recognition
Author:
Affiliation:
1. Institution National Digital Switching System Engineering & Technological Research Centre, Zheng Zhou, China
2. Institution National Digital Switching System Engineering & Technological Research Centre, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3208788.3208798
Reference21 articles.
1. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
2. EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding
3. Miao Y. and Metze F. 2017. End-to-End Architectures for Speech Recognition. In New Era for Robust Speech Recognition 299--323. Miao Y. and Metze F. 2017. End-to-End Architectures for Speech Recognition. In New Era for Robust Speech Recognition 299--323.
4. Minimum Bayes risk training of CTC acoustic models in maximum a posteriori based decoding framework
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