Denoising autoencoder is applied to reverberant speech recognition as a noise robust front-end to reconstruct clean speech spectrum from noisy input.
Experimental results show that the proposed denoising autoencoder based front-end using the shortwindowed spectra gives better results than conventional ...
In this paper we present a deep denoising autoencoder (DDA) framework that can produce robust speech features for noisy reverberant speech recognition. The DDA ...
... In this paper, we propose a third class of single-channel late reverberation PSD estimators based on denoising autoencoders (DAs) [12,13]. In the context of ...
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In this paper we present a deep denoising autoencoder (DDA) framework that can produce robust speech features for noisy reverberant speech recognition.
Experimental results show that DAEME is superior to several baseline models in terms of objective evaluation metrics, automatic speech recognition results, and ...
A deep denoising autoencoder (DDA) framework that can produce robust speech features for noisy reverberant speech recognition and shows a 16-25% absolute ...
When speech recognition task is done by an automatic speech recognition system (ASR), it always has to process the noise and reverberation mixed with the ...
May 12, 2015 · Deep neural network (DNN)-based approaches have been shown to be effective in many automatic speech recognition systems.
Abstract. This paper describes our joint efforts to provide robust automatic speech recognition (ASR) for reverberated environments, such as in hands-free ...