Adaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system

The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a combined features is presents. In this paper, a wavelet thresholding pre-processing stage, and feature warping (FW) techniques are used with two combined features named power normalized cepstral coefficients (PNCC) and gammatone frequency cepstral coefficients (GFCC) to improve the identification system robustness against different types of additive noises. Universal Background Model Gaussian Mixture Model (UBM-GMM) is used for features matching between the claim and actual speakers. The results showed performance improvement for the proposed feature extraction algorithm of identification system comparing with conventional features over most types of noises and different SNR ratios

Progressive loss functions for speech enhancement with deep neural networks

The progressive paradigm is a promising strategy to optimize network performance for speech enhancement purposes. Recent works have shown different strategies to improve the accuracy of speech enhancement solutions based on this mechanism. This paper studies the progressive speech enhancement using convolutional and residual neural network architectures and explores two criteria for loss function optimization: weighted and uniform progressive. This work carries out the evaluation on simulated and real speech samples with reverberation and added noise using REVERB and VoiceHome datasets. Experimental results show a variety of achievements among the loss function optimization criteria and the network architectures. Results show that the progressive design strengthens the model and increases the robustness to distortions due to reverberation and noise

On the application of minimum noise tracking to cancel cosine shaped residual noise

It has been shown recently that for coherence based dual microphone array speech enhancement systems, cross-spectral subtraction is an efficient technique aimed to reduce the correlated noise components. The zero-phase filtering criterion employed in these methods is derived from the standard coherence function that is modified to incorporate the noise cross power spectrum between the two channels. However, there has been limited success at applying coherence based filters when speech processing is carried out under relatively harsh acoustic conditions (SNR below -5dB) or when the speech and noise sources are closely spaced. We propose an alternative method that is effective, and that attempts to use a phase-based filtering criterion by substituting the cross power spectrum of the noisy signals received on the two channels by its real part. Then, a variant of the running minimum noise tracking procedure is applied on the estimated speech spectrum as an adaptive postfiltering to reduce the cosine shaped power spectrum of the remaining residual musical noise to a minimum spectral floor. Using that adaptive postfilter, a softdecision scheme is implemented to control the amount of noise suppression. Our preliminary results based on experiments conducted on real speech signals show an improved performance of the proposed method over the coherence based approaches. These results also show that it performs well on speech while producing less spectral distortion even in severe noisy conditions