Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers
Author:
Affiliation:
1. RWTH Aachen University,Human Language Technology and Pattern Recognition,Computer Science Department,Aachen,Germany,52074
Funder
Ministry of Health
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10094609.pdf?arnumber=10094609
Reference33 articles.
1. Minimum Word Error Rate Training for Attention-Based Sequence-to-Sequence Models
2. Efficient Minimum Word Error Rate Training of RNN-Transducer for End-to-End Speech Recognition
3. Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI
4. Acoustic Modelling with CD-CTC-sMBR LSTM RNNs;senior;IEEE 2015 Automatic Speech Recognition and Understanding Workshop (ASRU),2015
5. Conformer: Convolution-augmented Transformer for Speech Recognition
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1. End-to-End Speech Recognition: A Survey;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024
2. Investigating The Effect of Language Models in Sequence Discriminative Training For Neural Transducers;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16
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