HMM vs. CTC for Automatic Speech Recognition: Comparison Based on Full-Sum Training from Scratch
Author:
Affiliation:
1. RWTH Aachen University,Human Language Technology and Pattern Recognition Group,Germany
Funder
BMBF
DFG
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10022052/10022330/10022967.pdf?arnumber=10022967
Reference29 articles.
1. Librispeech: An ASR corpus based on public domain audio books
2. SWITCHBOARD: telephone speech corpus for research and development
3. Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition
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1. Strong Label Generation for Preparing Speech Data in Military Applications Using CTC Loss;2024 International Conference on Military Communication and Information Systems (ICMCIS);2024-04-23
2. Exploring Dominant Paths in CTC-Like ASR Models: Unraveling the Effectiveness of Viterbi Decoding;2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW);2024-04-14
3. End-to-End Speech Recognition: A Survey;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024
4. End-To-End Training of a Neural HMM with Label and Transition Probabilities;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16
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