Towards end-to-end speech recognition with transfer learning

Towards end-to-end speech recognition with transfer learning

Abstract A transfer learning-based end-to-end speech recognition approach is presented in two levels in our framework. Firstly, a feature extraction approach combining multilingual deep neural network (DNN) training with matrix factorization algorithm is introduced to extract high-level features. Se...

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Bibliographic Details
Main Authors: Chu-Xiong Qin, Dan Qu, Lian-Hai Zhang
Format: Article
Language:English
Published: SpringerOpen 2018-11-01
Series:EURASIP Journal on Audio, Speech, and Music Processing
Subjects:
Speech recognition
End-to-end
Transfer learning
Online Access:http://link.springer.com/article/10.1186/s13636-018-0141-9

Internet

http://link.springer.com/article/10.1186/s13636-018-0141-9

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