6 accepted papers
In this paper, we present a factored network-based acoustic modeling framework with various deep convolutional recurrent neural network (RNN) architectures for noise-robust automatic speech recognition (ASR). As the factored network-based acoustic model, we have already proposed a deep convolutional…
In the recent years, discriminative models have become a very attractive utility and gained a lot of attention in the speech research community, encompassing both front and back-end methods, thanks to their prominent discriminative power and the availability of improved training strategies. When it…
This paper presents multi-pass feature enhancement technique that consists of three processing passes. In the proposed method, the first pass was described in our previous work, and consists of model-based feature enhancement realized by employing a generative-discriminative hybrid approach with Gau…
We propose a technique of multi-channel speech enhancement based on integration of beamforming and statistical model-based speech enhancement to clearly extract the target speech, even in very noisy environments. Conventional microphone array-based techniques estimate speech and noise power spectral…
This paper investigates a multi-channel denoising autoencoder (DAE)-based speech enhancement approach. In recent years, deep neural network (DNN)-based monaural speech enhancement and robust automatic speech recognition (ASR) approaches have attracted much attention due to their high performance. Al…
This paper presents a technique that combines generative and discriminative approaches with Gaussian mixture models (GMMs) and deep neural networks (DNNs) for model-based feature enhancement. Typical model-based feature enhancement employs a generative model approach. The enhanced features are obtai…