Dongfu Li and Martin Bouchard, University of Ottawa, Canada

Single-channel speech intelligibility enhancement is much more difficult than multi-channel intelligibility enhancement. It has recently been reported that machine learning training-based single-channel speech intelligibility enhancement algorithms perform better than traditional algorithms. In this paper, the performance of a deep neural network method using a multiresolution cochlea-gram feature set recently proposed to perform single-channel speech intelligibility enhancement processing is evaluated. Various conditions such as different speakers for training and testing as well as different noise conditions are tested. Simulations and objective test results show that the method performs better than another deep neural networks setup recently proposed for the same task, and leads to a more robust convergence compared to a recently proposed Gaussian mixture model approach.

Single-channel speech intelligibility enhancement processing, Deep Neural Networks (DNN), Multi-Resolution CochleaGram (MRCG), Gaussian Mixture Models (GMM)