Consistent Training and Decoding for End-to-End Speech Recognition Using Lattice-Free MMI
Author:
Affiliation:
1. Peking University,ADSPLAB, School of ECE,Shenzhen,China
2. Tencent AI Lab
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09746579.pdf?arnumber=9746579
Reference23 articles.
1. End-to-end Speech Recognition Using Lattice-free MMI
2. Minimum Word Error Rate Training for Attention-Based Sequence-to-Sequence Models
3. Improving Attention Based Sequence-to-Sequence Models for End-to-End English Conversational Speech Recognition
4. Minimum bayes risk training of rnn-transducer for end-to-end speech recognition;weng;Proc Interspeech 2020,2019
5. Efficient Minimum Word Error Rate Training of RNN-Transducer for End-to-End Speech Recognition
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1. Controllable Accented Text-to-Speech Synthesis With Fine and Coarse-Grained Intensity Rendering;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
3. Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04
4. Integrating Lattice-Free MMI Into End-to-End Speech Recognition;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2023
5. Towards Unified All-Neural Beamforming for Time and Frequency Domain Speech Separation;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2023
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