In this article, we propose a method called “continuous noise masking (cNM)” that allows eliminating residual buzziness in a continuous vocoder, i.e. of which all parameters are continuous and offers a simple and flexible speech analysis and synthesis system. Traditional parametric vocoders generally show a perceptible deterioration in the quality of the synthesized speech due to different processing algorithms. Furthermore, an inaccurate noise resynthesis (e.g. in breathiness or hoarseness) is also considered to be one of the main underlying causes of performance degradation, leading to noisy transients and temporal discontinuity in the synthesized speech. To overcome these issues, a new cNM is developed based on the phase distortion deviation in order to reduce the perceptual effect of the residual noise, allowing a proper reconstruction of noise characteristics, and model better the creaky voice segments that may happen in natural speech. To this end, the cNM is designed to keep only voice components under a condition of the cNM threshold while discarding others. We evaluate the proposed approach and compare with state-of-the-art vocoders using objective and subjective listening tests. Experimental results show that the proposed method can reduce the effect of residual noise and can reach the quality of other sophisticated approaches like STRAIGHT and log domain pulse model (PML).

In this letter, we propose a new approach to estimate the degree of noise masking based on a sophisticated model for clean speech distribution. This measure, named as noise masking probability (NMP), is incorporated into the feature compensation technique to achieve robust speech recognition in noisy environments. Experimental results show that the proposed approach improves the performance of the baseline recognition system in the presence of various background noises.

Employing noise masking threshold (NMT) to adapt a speech enhancement system has become popular due to the advantage of rendering the residual noise to perceptually white. Most methods employ the NMT to empirically adjust the parameters of a speech enhancement system according to the various properties of noise. In this article, without any predefined empirical factor, an explicit-form gain factor for a frequency bin is derived by perceptually constraining the residual noise below the NMT in spectral domain. This perceptual constraint preserves the spectrum of noisy speech when the level of residual noise is less than the NMT. If the level of residual noise exceeds the NMT, then the spectrum of noisy speech is suppressed to reduce the corrupting noise. Experimental results show that the proposed approach can efficiently remove the added noise in cases of various noise corruptions, and almost free from musical residual noise.