In this paper, we propose a new speech enhancement system using the wavelet thresholding algorithm. The basic wavelet thresholding algorithm has some defects including the assumption of white Gaussian noise (WGN), malfunction in unvoiced segments, bad auditory quality, etc. In the proposed system, we introduce a new algorithmwhich does not require any voiced/ unvoiced detection system.Also, in ...

Article history: Received 12 November 2013 Received in revised form 24 June 2014 Accepted 25 June 2014

One implicit assumption the speech enhancement algorithms is that the representation of speech in a transform domain or over a redundant dictionary is sparse, while that of noise is dense. Based on this assumption, clean speech can be recovered by finding the sparse representations. However, some kinds of noise are also found sparse in the above representation scenarios, which results in degrad...

Speech enhancement is a sustained standing issue with various applications like automatic recognition, coding of speech signals and hearing aids. Single channel speech enhancement approach is used for enhancement of effective speech depraved by additive backdrop noises. The backdrop noise can have the conflicting impact on our capability to converse without interruption or fluently in very nois...

In this paper, we propose a novel approach for single channel speech enhancement by exploiting the correlation among 2D transform coefficients, which has been previously neglected by traditional speech enhancement methods. Our approach makes use of a time-frequency representation (spectrogram) of the input signal and a novel 2D spectrogram filter (2DSF)is designed to provide a good estimate of ...

Performance of speech recognition systems strongly degrades in the presence of background noise, like the driving noise inside a car. In contrast to existing works, we aim to improve noise robustness focusing on all major levels of speech recognition: feature extraction, feature enhancement, speech modelling, and training. Thereby, we give an overview of promising auditory modelling concepts, s...

In this contribution we optimize a speech enhancement preprocessor such that a distortion measure in the Line Spectral Frequency (LSF) domain is minimized. We can thus improve the estimation of spectral parameters of a speech coder when the input signal to the coder is a noisy speech signal. The optimization aims at the maximum noise reduction of the enhancement preprocessor. The average maximu...

Performance of speech recognition systems strongly degrades in the presence of background noise, like the driving noise in the interior of a car. We aim to improve noise robustness focusing on all major levels of speech recognition: feature extraction, feature enhancement, and speech modeling. Different auditory modeling concepts, speech enhancement techniques, training strategies, and model ar...

Speech Enhancement is a challenging and important area of research due to the many applications that depend on improved signal quality. It is a pre-processing step of speech processing systems and used for perceptually improving quality of speech for humans. With recent advances in Deep Neural Networks (DNN), deep Denoising Auto-Encoders have proved to be very successful for speech enhancement....

Models for automatic speech recognition (ASR) hold detailed information about spectral and spectro-temporal characteristics of clean speech signals. Using these models for speech enhancement is desirable and has been the target of past research efforts. In such model-based speech enhancement systems, a powerful ASR is imperative. To increase the recognition rates especially in low-SNR condition...