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A reverberation-time-aware deep-neural-network (DNN)-based speech dereverberation framework is proposed to handle a wide range of reverberation times. There are three key steps in designing a robust system. First, in contrast to sigmoid activation and min–max normalization in state-of-the-art algorithms, a linear activation function at the output layer and global mean-variance normalization of target...
We propose a unified deep neural network (DNN) approach to achieve both high-quality enhanced speech and high-accuracy automatic speech recognition (ASR) simultaneously on the recent REverberant Voice Enhancement and Recognition Benchmark (RE-VERB) Challenge. These two goals are accomplished by two proposed techniques, namely DNN-based regression to enhance reverberant and noisy speech, followed by...
We investigate the effects of time and frequency sampling on short-time Fourier transform modifications to be used for speech dereverberation based on deep neural networks (DNNs). We first show that by adopting a linear activation function at the output layer and globally normalizing the target features into zero mean and unit variance, better performances can be obtained than existing DNN approaches...
We adopt a linear activation function at the output layer and globally normalize the target features into zero mean and unit variance to learn the complicated mapping from reverberant to anechoic speech with a regression model based on deep neural networks (DNNs). The proposed feature activation and normalization framework was found to retain clearly observable harmonics and improve the speech quality...
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