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The reverberant speech segregation is a basic problem in speech enhancement and automatic speech recognition. Based on the deep neural networks (DNN), a novel binaural speech segregation method is proposed. The binaural feature is extracted and used as the cue to train a DNN with a ideal parameter mask. The trained DNN is used to distinguish the target speech and noise, and output the estimated parameter...
Within the framework of computational auditory scene analysis (CASA), a parameter masks estimator based on deep neural networks (DNN) is proposed for automatic speech recognition (ASR) in noisy environments. This paper addresses the robustness in binaural machine speech recognition by speech energy estimation using DNN. An ideal parameter mask (IPM) is introduced as the goal of the DNN estimator,...
The speech segregation and enhancement is a hard task in speech communication. In order to get the clean target speech, a close talk system is used to collect the speech with a nearby microphone. A deep neural networks (DNN) estimator is used in a frequency channel for speech energy calculation with parameter masks. The adjusted binaural auditory features are used as the main input for DNN speech...
Gammatone filterbanks are widely used in computational auditory models for modeling the peripheral filtering function of the cochlea. However, the high computational complexity and time consumption limits its usage in portable acoustic applications. To address this issue, a realtime and efficient digital implementation of Gammatone filterbank is proposed. The decomposed signal can be resynthesized...
Robust speech enhancement is a challenge task, especially in noisy environments. The deep neural network has shown good performance on binaural speech enhancement with various speakers at a same distance. As binaural cues are based on the locations of sound sources, this paper analyze the performance of binaural deep neural network with different distances. The theoretical derivation and experiment...
Speech signal degradation in real environments mainly results from room reverberation and concurrent noise. While human listening is robust in complex auditory scenes, current speech segregation algorithms do not perform well in noisy and reverberant environments. We treat the binaural segregation problem as binary classification, and employ deep neural networks (DNNs) for the classification task...
A major challenge for automatic speech recognition (ASR) relates to significant performance reduction in noisy environments. Recent research has shown that auditory features based on Gammatone filters are promising to improve robustness of ASR systems against noise, though the research is far from extensive and generalizability of the new features is unknown. This paper presents our implementation...
Monaural speech segregation from complex concurrent noise is an extremely challenging problem; binary mask is a method to solve this problem, however, the performance of binary mask is limited by remaining the noise in the result. In this paper, an algorithm integrated Spectral Subtraction and binary masking for speech separation and enhancement was proposed. It follows the framework of computational...
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