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Learning Decoupling Features Through Orthogonality Regularization
- Source :
- ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
- Publication Year :
- 2022
- Publisher :
- IEEE, 2022.
-
Abstract
- Keyword spotting (KWS) and speaker verification (SV) are two important tasks in speech applications. Research shows that the state-of-art KWS and SV models are trained independently using different datasets since they expect to learn distinctive acoustic features. However, humans can distinguish language content and the speaker identity simultaneously. Motivated by this, we believe it is important to explore a method that can effectively extract common features while decoupling task-specific features. Bearing this in mind, a two-branch deep network (KWS branch and SV branch) with the same network structure is developed and a novel decoupling feature learning method is proposed to push up the performance of KWS and SV simultaneously where speaker-invariant keyword representations and keyword-invariant speaker representations are expected respectively. Experiments are conducted on Google Speech Commands Dataset (GSCD). The results demonstrate that the orthogonality regularization helps the network to achieve SOTA EER of 1.31% and 1.87% on KWS and SV, respectively.<br />Comment: Accepted at ICASSP 2022
- Subjects :
- FOS: Computer and information sciences
Sound (cs.SD)
Artificial Intelligence (cs.AI)
Computer Science - Artificial Intelligence
Audio and Speech Processing (eess.AS)
FOS: Electrical engineering, electronic engineering, information engineering
Computer Science - Sound
Electrical Engineering and Systems Science - Audio and Speech Processing
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Accession number :
- edsair.doi.dedup.....c77d8f20ce0632fcfca82aed239f79cc