3.1 Introduction 3.2 Estimation Based Filtering Techniques

  • Published 2015


In general, there exists a need for voice based communications, human-machine/ machine-machine interfaces, and automatic speech recognition systems to increase the reliably of these systems in noisy environments. In many cases, these systems work well in nearly noise-free conditions, but their performance deteriorates rapidly in noisy conditions. Therefore, improvement in existing pre-processing algorithms or introducing entire new class for algorithm for speech enhancement is always the objective of research community. The main requirement for speech enhancement systems varies according to specific applications, such as to boost the overall speech quality, to increase intelligibility, and to improve the performance of voice communication devices. One of the early papers [1] in speech enhancement considers the problem of estimation of speech parameters from the speech, which has been degraded by additive background noise. In this work they propose the two suboptimal procedures which have linear iterative implementations in order to suppress the non-linear effect on the speech parameters due to background noise. In another similar problem [2] of enhancing the speech in presence of additive acoustic noise, spectral decomposition of frame of noisy speech was adopted. The attenuation of particular spectral component was determined based o n how much the measured speech plus noise power exceeds an estimation of background noise leading an importance of proper choice of the suppression or subtraction factors. The short-time spectral amplitude (STSA) was used to model the speech and noise spectral components in [3]. The parametric estimation techniques, where parameters of underlying model, consist of small set of parameters, is determined and then numerical process is used to modify the parameters, can be contrasted by the non-parametric method which can be used as in [4] where no model is assumed and uses non-parametric spectrum estimation techniques. In application point of view, there is work described in [5], where noisy speech enhancement algorithm has been discussed and implemented to compare its performance against the various levels of LPC (Linear Predictive coefficient) perturbation. Various speech enhancement techniques have been considered here such as spectral subtraction, spectral over subtraction with use of a spectral floor, spectral subtraction with residual noise removal and time and frequency domain adaptive MMSE filtering. The speech signal sued here for recognition experimentation was a typical sentence with additive normally distributed white noise distortion. The single channel speech enhancement algorithm at very low SNR has been presented in [6], which uses masking properties of human auditory …

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@inproceedings{201531I3, title={3.1 Introduction 3.2 Estimation Based Filtering Techniques}, author={}, year={2015} }