Clean Speech Components Research Articles

A noise PSD estimation algorithm using derivative-based high-pass filter in non-stationary noise conditions

The minimum mean-square error (MMSE)-based noise PSD estimators have been used widely for speech enhancement. However, the MMSE noise PSD estimators assume that the noise signal changes at a slower rate than the speech signal— which lacks the ability to track the highly non-stationary noise sources. Moreover, the performance of the MMSE-based noise PSD estimator largely depends upon the accuracy of the a priori SNR estimation in practice. In this paper, we introduce a noise PSD estimation algorithm using a derivative-based high-pass filter in non-stationary noise conditions. The proposed method processes the silent and speech frames of the noisy speech differently to estimate the noise PSD. It is due to the non-stationary noise that can be mixed with silent and speech-dominated frames non-uniformly. We first introduce a spectral-flatness-based adaptive thresholding technique to detect the speech activity of the noisy speech frames. Since the silent frame of the noisy speech is completely filled with noise, the noise periodogram is directly computed from it without applying any filtering. Conversely, a 4th order derivative-based high-pass filter is applied during speech activity of the noisy speech frame to filter out the clean speech components while leaving behind mostly the noise. The noise periodogram is computed from the filtered signal—which counteracts the leaking of clean speech power. The noise PSD estimate is obtained by recursively averaging the previously estimated noise PSD and the current estimate of the noise periodogram. The proposed method is found to be effective in tracking the rapidly changing as well as the slowly varying noise PSD than the competing methods in non-stationary noise conditions for a wide range of signal-to-noise ratio (SNR) levels. Extensive objective and subjective scores on the NOIZEUS corpus demonstrate that the application of the proposed noise PSD with MMSE-based speech enhancement methods produce higher quality and intelligible enhanced speech than the competing methods.

Model-based clustered sparse imputation for noise robust speech recognition

In the sparse imputation approach, missing spectral components of speech are estimated using the compressive sensing technique. For this purpose, a dictionary of clean speech components must be prepared. Noisy feature vectors are then reconstructed by the dictionary queries. In this approach, the dictionary elements should adequately cover all the possible varieties of the speech feature vectors; so, for a subject speech frame, there will be lots of irrelevant components inside the dictionary. These components make a huge size for the dictionary that in turn, slow down the estimation process and may produce artifacts in the final estimation. To face this problem, the current work proposes to cluster the dictionary queries in some smaller subspaces; the relevant subspace for a subject feature vector could be found through the posterior criterion. Moreover, this is shown that the likelihood of the Gaussian models developed for the subspaces could role as a regularization term and act as an extra prior knowledge in the estimation process and increases the final performance, significantly. To evaluate the benefits of the proposed methods, some well-designed ASR experiments are conducted on two different speech corpora, an English noisy connected digit database (Aurora 2) and a Persian continuous speech corpus (FARSDAT). The experiments show that the proposed methods not only increase the absolute word recognition accuracy but also make the entire process few times faster than the original sparse imputation approach.

A single channel speech enhancement technique exploiting human auditory masking properties

Abstract. To enhance extreme corrupted speech signals, an Improved Psychoacoustically Motivated Spectral Weighting Rule (IPMSWR) is proposed, that controls the predefined residual noise level by a time-frequency dependent parameter. Unlike conventional Psychoacoustically Motivated Spectral Weighting Rules (PMSWR), the level of the residual noise is here varied throughout the enhanced speech based on the discrimination between the regions with speech presence and speech absence by means of segmental SNR within critical bands. Controlling in such a way the level of the residual noise in the noise only region avoids the unpleasant residual noise perceived at very low SNRs. To derive the gain coefficients, the computation of the masking curve and the estimation of the corrupting noise power are required. Since the clean speech is generally not available for a single channel speech enhancement technique, the rough clean speech components needed to compute the masking curve are here obtained using advanced spectral subtraction techniques. To estimate the corrupting noise, a new technique is employed, that relies on the noise power estimation using rapid adaptation and recursive smoothing principles. The performances of the proposed approach are objectively and subjectively compared to the conventional approaches to highlight the aforementioned improvement.

Speech enhancement using an MMSE short time DCT coefficients estimator with supergaussian speech modeling

In this paper, two speech enhancement systems with supergaussian speech modeling are presented. The clean speech components are estimated by Minimum-Mean-Square-Error (MMSE) estimator under the assumption that the DCT coefficients of clean speech are modeled by a Laplacian or a Gamma distribution and the DCT coefficients of the noise are Gaussian distributed. Then, MMSE estimators under speech presence uncertainty are derived. Furthermore, the proper estimators of the speech statistical parameters are proposed. The speech Laplacian factor is estimated by a new decision-directed method. The simulation results show that the proposed algorithm yields less residual noise and better speech quality than the Gaussian based speech enhancement algorithms proposed in recent years.

Linear Dynamic Models With Mixture of Experts Architecture for Recognition of Speech Under Additive Noise Conditions

This letter presents a new approach to enhance speech feature estimation in the log spectral domain under noisy environments. A mixture of linear dynamic models with an architecture similar to the so-called mixture of experts (ME) is investigated to describe the clean speech feature distribution parametrically. Switching Kalman filters are adapted to the proposed model, and they estimate the clean speech components by means of a generalized pseudo-Bayesian (GPB) algorithm. Experimental results suggest that compared with previous methods, the proposed approach can be more powerful to compensate the noisy speech features for robust speech recognition

Speech enhancement employing Laplacian-Gaussian mixture

A new, efficient speech enhancement algorithm (SEA) is developed in this paper. In this low-complexity SEA, a noisy speech signal is first decorrelated and then the clean speech components are estimated from the decorrelated noisy speech samples. The distributions of clean speech and noise signals are assumed to be Laplacian and Gaussian, respectively. The clean speech components are estimated either by maximum likelihood (ML) or minimum-mean-square-error (MMSE) estimators. These estimators require some statistical parameters derived from speech and noise. These parameters are adaptively extracted by the ML approach during the active speech or silence intervals, respectively. In addition, a voice activity detector (VAD) that uses the same statistical model is employed to detect whether the speech is active or not. The simulation results show that our SEA approach performs as well as a recent high efficiency SEA that employs the Wiener filter. The computational complexity of this algorithm is very low compared with existing SEAs with low computational complexity.

Feature compensation based on switching linear dynamic model

In this letter, we propose a novel approach to feature compensation for robust speech recognition in noisy environments. We employ the switching linear dynamic model (SLDM) as a parametric model for the clean speech distribution, which enables us to exploit temporal correlations inherent in speech signals. Both the background noise and clean speech components are simultaneously estimated by means of the interacting multiple model (IMM) algorithm.