Add to ORKGDeep neural networks (DNN) have recently been shown to give state-of-the-art performance in monaural speech enhancement. However in the DNN training process, the perceptual difference between different components of the DNN output is not fully exploited, where equal importance is often assumed. To address this limitation, we have proposed a new perceptually-weighted objective function within a feedforward DNN framework, aiming to minimize the perceptual difference between the enhanced speech and the target speech. A perceptual weight is integrated into the proposed objective function, and has been tested on two types of output features: spectra and ideal ratio masks. Objective evaluations for both speech quality and speech intelligibility h...
In this work, we investigate the problem of speaker independent acoustic-to-articulatory inversion (...
In this work, we propose a novel representationlearning technique for Deep Learning-based Speech Enh...
Advancements in machine learning techniques have promoted the use of deep neural networks (DNNs) for...
Deep neural networks (DNN) have recently been shown to give state-of-the-art performance in monaura...
Abstract—This letter presents a regression-based speech en-hancement framework using deep neural net...
This master thesis describes the implementation and evaluation of a promising approach to speech enh...
We propose to use a perceptually-oriented domain to improve the quality of text-to-speech generated ...
In the last years, deep neural networks have become an important tool in speech technologies, yield...
Speech enhancement, which aims to recover the clean speech of the corrupted signal, plays an importa...
This letter proposes a perceptual metric for speech quality evaluation, which is suitable, as a loss...
Abstract The performance of the existing speech enhancement algorithms is not ideal in low signal-to...
Speech intelligibility represents how comprehensible a speech is. It is more important than speech q...
Speech enhancement systems aim to improve the quality and intelligibility of noisy speech. In this s...
In this paper, we considered the problem of the speech enhancement similar to the real-world environ...
In contrast to classical noise reduction methods introduced over the past decades, this work focuses...
In this work, we investigate the problem of speaker independent acoustic-to-articulatory inversion (...
In this work, we propose a novel representationlearning technique for Deep Learning-based Speech Enh...
Advancements in machine learning techniques have promoted the use of deep neural networks (DNNs) for...
Deep neural networks (DNN) have recently been shown to give state-of-the-art performance in monaura...
Abstract—This letter presents a regression-based speech en-hancement framework using deep neural net...
This master thesis describes the implementation and evaluation of a promising approach to speech enh...
We propose to use a perceptually-oriented domain to improve the quality of text-to-speech generated ...
In the last years, deep neural networks have become an important tool in speech technologies, yield...
Speech enhancement, which aims to recover the clean speech of the corrupted signal, plays an importa...
This letter proposes a perceptual metric for speech quality evaluation, which is suitable, as a loss...
Abstract The performance of the existing speech enhancement algorithms is not ideal in low signal-to...
Speech intelligibility represents how comprehensible a speech is. It is more important than speech q...
Speech enhancement systems aim to improve the quality and intelligibility of noisy speech. In this s...
In this paper, we considered the problem of the speech enhancement similar to the real-world environ...
In contrast to classical noise reduction methods introduced over the past decades, this work focuses...
In this work, we investigate the problem of speaker independent acoustic-to-articulatory inversion (...
In this work, we propose a novel representationlearning technique for Deep Learning-based Speech Enh...
Advancements in machine learning techniques have promoted the use of deep neural networks (DNNs) for...