Semi-Supervised Training of Transformer and Causal Dilated Convolution Network with Applications to Speech Topic Classification

Aiming at the audio event recognition problem of speech recognition, a decision fusion method based on the Transformer and Causal Dilated Convolutional Network (TCDCN) framework is proposed. This method can adjust the model sound events for a long time and capture the time correlation, and can effec...

Full description

Saved in:
Bibliographic Details
Published inApplied sciences Vol. 11; no. 12; p. 5712
Main Authors Zeng, Jinxiang, Zhang, Du, Li, Zhiyi, Li, Xiaolin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2021
Subjects
Accuracy
Acoustics
Audio data
automatic speech recognition
Classification
Convolution
Deep learning
Neural networks
Noise
semi-supervised learning
semi-supervised training
Speech
Speech recognition
topic classification
Transformer and Causal Dilated Convolution Network
Voice recognition
Online AccessGet full text

Cover

More Information
Summary:Aiming at the audio event recognition problem of speech recognition, a decision fusion method based on the Transformer and Causal Dilated Convolutional Network (TCDCN) framework is proposed. This method can adjust the model sound events for a long time and capture the time correlation, and can effectively deal with the sparsity of audio data. At the same time, our dataset comes from audio clips cropped by YouTube. In order to reliably and stably identify audio topics, we extract different features and different loss function calculation methods to find the best model solution. The experimental results from different test models show that the TCDCN model proposed in this paper achieves better recognition results than the classification using neural networks and other fusion methods.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11125712