Improving end-to-end transformer model architecture in ASR
As a result of advancement in deep learning and neural network technology, end-to-end models have been introduced into automatic speech recognition (ASR) successfully and achieved superior performance compared to conventional hybrid systems. End-to-end models simplify the traditional GMM-HMM models...
Saved in:
Main Author: | Zhao, Yingzhu |
---|---|
Other Authors: | Chng Eng Siong |
Format: | Thesis-Doctor of Philosophy |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/166407 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
End-to-end autonomous driving based on reinforcement learning
by: Ong, Chee Wei
Published: (2022) -
Predicting the highest and lowest stock price before end of the day
by: Choo, Zhi Cheng
Published: (2014) -
GDCEngine: an end-to-end machine learning engine for green data center control optimization with digital twins
by: Zhang, Xinyi
Published: (2022) -
End-to-end deep reinforcement learning for multi-agent collaborative exploration
by: Chen, Zichen, et al.
Published: (2021) -
Vision transformer as image fusion model
by: Zhao, Fengye
Published: (2023)