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

• Published in: Applied sciences Vol. 11; no. 12; p. 5712
• Main Authors: Zeng, Jinxiang, Zhang, Du, Li, Zhiyi, Li, Xiaolin
• Format: Journal Article
• Language: English
• 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
• DOI: 10.3390/app11125712
• ISSN: 2076-3417

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.