Codebook-based Bayesian speech enhancement for nonstationary environments

In this paper, we propose a Bayesian minimum mean squared error approach for the joint estimation of the short-term predictor parameters of speech and noise, from the noisy observation. We use trained codebooks of speech and noise linear predictive coefficients to model the a priori information required by the Bayesian scheme. In contrast to current Bayesian estimation approaches that consider the excitation variances as part of the a priori information, in the proposed method they are computed online for each short-time segment, based on the observation at hand. Consequently, the method performs well in nonstationary noise conditions. The resulting estimates of the speech and noise spectra can be used in a Wiener filter or any state-of-the-art speech enhancement system. We develop both memoryless (using information from the current frame alone) and memory-based (using information from the current and previous frames) estimators. Estimation of functions of the short-term predictor parameters is also addressed, in particular one that leads to the minimum mean squared error estimate of the clean speech signal. Experiments indicate that the scheme proposed in this paper performs significantly better than competing method

Review of Noise Reduction Techniques in Speech Processing

Present systems advances in speech processing systems aim at providing sturdy and reliable interfaces for sensible preparation. Achieving sturdy performance of those systems in adverse and screeching environments is one in every of the most important challenges in applications like dictation, voice-controlled devices, human-computer dialog systems and navigation systems. Performance of speech recognition systems powerfully degrades within the presence of background, just like the driving noise within a automobile. In distinction to existing works, we have a tendency to reduce the boost in noise strength that present in levels of speech recognition: feature extraction, feature improvement, speech modelling, and coaching. Thereby, we offer a summary of noise modelling ideas, speech improvement techniques, coaching ways, and model design, that square measure enforced in speech orthography recognition task considering noises created by numerous conditions. DOI: 10.17762/ijritcc2321-8169.15075