A Modified Deep Neural Network Speech Enhancement Model
| Autor: | Chi-hui Lin, 林季暉 |
|---|---|
| Rok vydání: | 2016 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 104 Speech intelegibility is more essential than before due to more and more mobile devices need to improve speech quality. This paper develops a more efficient deep neural network (DNN) speech enhancement model based upon Y. Xu’s DNN speech enhancement model, but the structure of DNNs and the learning algorithm of MLPs are modified to achieve an efficient DNN learning. A deep MLP neural net is composed by unrolling a stack of RBMs and adding on a layer to the last stage of the MLP. In the last layer, each neuron has a linear activation function with initial identity weights. Instead of the back-propagation, the resilient propagation learning is employed to train our DNN. These three modifications can speed up DNNs’ learning, that is verified with several experiments using NOIZEUS speech dataset. The key characteristics and mutual relationships among noise intensities, noise types, sentences and human gender, are also identified to reduce the size of training dataset. In order to effectively control parameters of our DNN speech enhancement model, correlations between learning results of DNNs and qualities of enhanced speech are analyzed. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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