Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection
Author:
Affiliation:
1. Chinese Academy of Sciences,Institute of Automation
2. Bytedance AI Lab
Funder
Chinese Academy of Sciences
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09747101.pdf?arnumber=9747101
Reference30 articles.
1. Conformer: Convolution-augmented Transformer for Speech Recognition
2. Contextual Speech Recognition in End-to-end Neural Network Systems Using Beam Search
3. Streaming End-to-end Speech Recognition for Mobile Devices
4. Shallow-Fusion End-to-End Contextual Biasing
5. Deep Context: End-to-end Contextual Speech Recognition
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2. LCB-Net: Long-Context Biasing for Audio-Visual Speech Recognition;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
3. Contextualized Automatic Speech Recognition With Attention-Based Bias Phrase Boosted Beam Search;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
4. SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
5. Locality Enhanced Dynamic Biasing and Sampling Strategies For Contextual ASR;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16
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