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In exemplar-based speech enhancement systems, lower dimensional features are preferred over the full-scale DFT features for their reduced computational complexity and the ability to better generalize for the unseen cases. But in order to obtain the Wiener-like filter for noisy DFT enhancement, the speech and noise estimates obtained in the feature space(More)
Exemplar-based techniques, where the noisy speech is decomposed as a linear combination of the speech and noise exemplars stored in a dictionary, have been successfully used for speech enhancement in noisy environments. This paper extends this technique to achieve speech dereverberation in noisy environments by means of a nonnegative approximation of the(More)
We propose a novel exemplar-based feature enhancement method for automatic speech recognition which uses coupled dictionaries: an input dictionary containing atoms sampled in the modulation (envelope) spectrogram domain and an output dictionary with atoms in the Mel or full-resolution frequency domain. The input modulation representation is chosen for its(More)
Exemplar-based speech enhancement systems work by decomposing the noisy speech as a weighted sum of speech and noise exemplars stored in a dictionary and use the resulting speech and noise estimates to obtain a time-varying filter in the full-resolution frequency domain to enhance the noisy speech. To obtain the decomposition, exemplars sampled in lower(More)
Deep neural network (DNN) based acoustic modelling has been successfully used for a variety of automatic speech recognition (ASR) tasks, thanks to its ability to learn higher-level information using multiple hidden layers. This paper investigates the recently proposed exemplar-based speech enhancement technique using coupled dictionaries as a pre-processing(More)
Deep neural network (DNN) based acoustic modelling has been shown to yield significant improvements over Gaussian Mixture Models (GMM) for a variety of automatic speech recognition (ASR) tasks. In addition, it is also becoming popular to use rich speech representations, such as full-resolution spectrograms and perceptually motivated features, as input to(More)
BACKGROUND Ethnomedicine is gaining admiration since years but still there is abundant medicinal flora which is unrevealed through research. The study was conducted to assess the in vitro antimicrobial potential and also determine the minimum inhibitory concentration (MIC) of Citrus sinensis peel extracts with a view of searching a novel extract as a remedy(More)
We present a novel automatic speech recognition (ASR) scheme which uses the recently proposed noise robust exemplar matching framework for speech enhancement in the front-end. The proposed system employs a GMM-HMM back-end to recognize the enhanced speech signals unlike the prior work focusing on template matching only. Speech enhancement is achieved using(More)
Exemplar-based feature enhancement successfully exploits a wide temporal signal context. We extend this technique with hybrid input spaces that are chosen for a more effective separation of speech from background noise. This work investigates the use of two different hybrid input spaces which are formed by incorporating the full-resolution and modulation(More)
In this paper, we propose a single-channel speech enhancement system based on the noise robust exemplar matching (N-REM) framework using coupled dictionaries. N-REM approximates noisy speech segments as a sparse linear combination of speech and noise exemplars that are stored in multiple dictionaries based on their length and associated speech unit. The(More)