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 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.

Automated summary

The TCDCN model proposed in this paper, based on the Transformer and Causal Dilated Convolutional Network framework, aims to address the audio event recognition problem in speech recognition. By adjusting model sound events, capturing time correlation, and effectively dealing with the sparsity of audio data, this model achieves better recognition results than classification using neural networks and other fusion methods.