In this paper, we provide an overview of some recently introduced principles and ideas for speech enhancement with linear filtering and explore how these are related and how they can be used in various applications. This is done in a general framework where the speech enhancement problem is stated as a signal vector estimation problem, i.e., with a filter matrix, where the estimate is obtained ...

Single channel speech enhancement is necessary where the multichannel speech enhancement is not feasible due to space constraints in the intended device and cost-effectiveness. However, the problem of having limited information from single channel sound signal mixtures or unavailability of the speech source signals makes it more difficult to separate the target speech from the background masker...

In this correspondence we illustrate how the (Auto:I, LSP:T) constrained iterative speech enhancement algorithm can be extended to provide improved performance in colored noise environments. The modified algorithm, referred to here as noise adaptive (Auto:I, LSP:T), operates on subbanded signal components in which the terminating iteration is adjusted based on the a posteriori estimate of the s...

Speech recognition errors affect the performance of multimodal systems especially in noisy conditions. For that reason the use of speech recognition confidence score and time stamps is suggested. This paper deals with the impact of a signal enhancement method on these speech recognition’s parameters, in the presence of noise. By recognising noisy signals not only the word error rate (WER) incre...

In this paper, the issue of single channel speech enhancement of non-stationary voiced speech is addressed. The non-stationarity of speech is well known, but state of the art speech enhancement methods assume stationarity within frames of 20–30 ms. We derive optimal distortionless filters that take the non-stationarity nature of voiced speech into account via linear constraints. This is facilit...

This paper proposes a speech signal enhancement method in which the wavelet transform scales and thresholds both are adaptive depending on the input noisy signal affected by Additive White Gaussian Noise (AWGN). The proposed Estimated Noise and Adaptive Threshold Bionic Wavelet Transform (ENAT-BWT) method analyses the incoming noisy speech signal at 22 scales, from 7 to 28, of the BWT for negat...

In recent years, speech development has become an interesting area in the field of signal processing especially in the applications of hearing aids. In hearing aid applications, the speech enhancement technique has been employed mainly for dipping the additive background noise. During the speech enhancement process, high background noise occurs due to the rapture nature of the speech. In order ...

Wavelet transform is an important tool used in many application areas. This paper proposed the analysis of noise reduction techniques which are based on wavelet transform. In this paper three noise reduction techniques based on wavelet transform are described. These methods are Wavelet Split Coefficient; Hard Thresholding and Soft Thresholding. MATLAB GUI is developed for visualization of resul...