標題: | A GENERATIVE AUDITORY MODEL EMBEDDED NEURAL NETWORK FOR SPEECH PROCESSING |
作者: | Lo, Yu-Wen Shen, Yih-Liang Liao, Yuan-Fu Chi, Tai-Shih 電機工程學系 Department of Electrical and Computer Engineering |
關鍵字: | generative auditory model;convolutional neural network;multi-resolution;speaker identification |
公開日期: | 1-一月-2018 |
摘要: | Before the era of the neural network (NN), features extracted from auditory models have been applied to various speech applications and been demonstrated more robust against noise than conventional speech-processing features. What's the role of auditory models in the current NN era? Are they obsolete? To answer this question, we construct a NN with a generative auditory model embedded to process speech signals. The generative auditory model consists of two stages, the stage of spectrum estimation in the logarithmic-frequency axis by the cochlea and the stage of spectral-temporal analysis in the modulation domain by the auditory cortex. The NN is evaluated in a simple speaker identification task. Experiment results show that the auditory model embedded NN is still more robust against noise, especially in low SNR conditions, than the randomly-initialized NN in speaker identification. |
URI: | http://hdl.handle.net/11536/150765 |
期刊: | 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
起始頁: | 5179 |
結束頁: | 5183 |
顯示於類別: | 會議論文 |