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

Minimum Mean-Squared Error Estimation of Mel-Frequency Cepstral Coefficients Using a Novel Distortion Model

In this paper, a new method for statistical estimation of Mel-frequency cepstral coefficients (MFCCs) in noisy speech signals is proposed. Previous research has shown that model-based feature domain enhancement of speech signals for use in robust speech recognition can improve recognition accuracy significantly. These methods, which typically work in the log spectral or cepstral domain, must face the high complexity of distortion models caused by the nonlinear interaction of speech and noise in these domains. In this paper, an additive cepstral distortion model (ACDM) is developed, and used with a minimum mean-squared error (MMSE) estimator for recovery of MFCC features corrupted by additive noise. The proposed ACDM-MMSE estimation algorithm is evaluated on the Aurora2 database, and is shown to provide significant improvement in word recognition accuracy over the baseline

Distributed Multichannel Speech Enhancement Based on Perceptually-Motivated Bayesian Estimators of the Spectral Amplitude

In this study, the authors propose multichannel weighted Euclidean (WE) and weighted cosh (WCOSH) cost function estimators for speech enhancement in the distributed microphone scenario. The goal of the work is to illustrate the advantages of utilising additional microphones and modified cost functions for improving signal-to-noise ratio (SNR) and segmental SNR (SSNR) along with log-likelihood ratio (LLR) and perceptual evaluation of speech quality (PESQ) objective metrics over the corresponding single-channel baseline estimators. As with their single-channel counterparts, the perceptually-motivated multichannel WE and WCOSH estimators are functions of a weighting law parameter, which influences attention of the noisy spectral amplitude through a spectral gain function, emphasises spectral peak (formant) information, and accounts for auditory masking effects. Based on the simulation results, the multichannel WE and WCOSH cost function estimators produced gains in SSNR improvement, LLR output and PESQ output over the single-channel baseline results and unweighted cost functions with the best improvements occurring with negative values of the weighting law parameter across all input SNR levels and noise types

Speech Enhancement using Beta-order MMSE Spectral Amplitude Estimator with Laplacian Prior

This report addresses the problem of speech enhancement employing the Minimum Mean-Square Error (MMSE) of β-order Short Time Spectral Amplitude (STSA). We present an analytical solution for β-order MMSE estimator where Discrete Fourier Transform (DFT) coefficients of (clean) speech are modeled by Laplacian distributions. Using some approximations for the joint probability density function and the Bessel function, we also present a closed-form version of the estimator (called β-order LapMMSE). The performance of the proposed estimator is compared to the state-of-the–art spectral amplitude estimators that assume Gaussian priors for clean DFT coefficients. Comparative results demonstrate the superiority of the proposed estimator in terms of speech enhancement/ noise reduction measures

A Subband Hybrid Beamforming for In-car Speech Enhancement

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201