Sharif Digital Repository

This paper studies a number of well-known noise estimation techniques and provides a comparative performance analysis of them in speech enhancement platform. Two types of evaluation data that simulate consistent and inconsistent noisy conditions are prepared in the presence of six noise types at different SNR levels. The performance of speech enhancement systems and the spectrum distance of the estimated and original noise spectrums are used as evaluation criteria. The evaluations indicate that a simple VAD method outperforms noise estimation methods in most of the consistent noisy conditions  

A new voice activity detection (VAD) algorithm with soft decision output in Mel-frequency domain is developed based on hidden Markov model (HMM) and is incorporated in an HMM-based speech enhancement system. The proposed VAD uses a two-state ergodic HMM representing speech presence and speech absence. The states are constructed from noisy speech and noise HMMs used in the speech enhancement system. This composite model provides a robust detection of speech segments in the presence of noise and obviates the need for extra modeling in HMM-based speech enhancement applications. As the main purpose of the proposed VAD is to detect speech segments accurately, a hang-over mechanism is proposed and... 

This paper addresses the problem of automatic speech recognition in real applications in which the speech signal is altered by various noises. Feature compensation and model compensation robustness methods are studied. Parallel model combination (PMC) and its recent advances are reviewed and a novel algorithm called PC-PMC is proposed. This algorithm utilizes cepstral mean subtraction (CMS) normalization ability and principal component analysis (PCA) compression and de-correlation capability in the combination with PMC model transformation method. PC-PMC algorithm takes the advantages of additive noise compensation ability of PMC and convolutional noise removal capability of CMS and PCA. In... 

Speech enhancement problem using hidden Markov model (HMM) and minimum mean square error (MMSE) in cepstral domain is studied. This noise reduction approach can be considered as weighted-sum filtering of the noisy speech signal in which the filters weights are estimated using the HMM of noisy speech. To have an accurate estimation of the noisy speech HMM, vector Taylor series (VTS) is proposed and compared with the parallel model combination (PMC) technique. Furthermore, proposed cepstral-domain HMM-based speech enhancement systems are compared with the renowned autoregressive HMM (AR-HMM) approach. The evaluation results confirm the superiority of the cepstral domain approach in comparison... 

An HMM-based speech enhancement in Mel-frequency domain is introduced and improved. It is shown that hidden Markov modeling in the Mel-frequency domain is beneficial due to its effective representation of the speech spectrum; however, speech enhancement in this domain requires an inversion from the Mel-frequency to the spectral domain which introduces distortion artifacts for spectrum estimation. To reduce the distortion effects of the inversion and employ the advantages of robustness modeling in the Mel-frequency domain, a parallel cepstral and spectral (PCS) modeling is proposed. In PCS, a concurrent modeling in both cepstral and spectral domains is performed. The performances of the... 

Hidden Markov model (HMM)-based minimum mean square error speech enhancement method in Mel-frequency domain is focused on and a parallel cepstral and spectral (PCS) modeling is proposed. Both Mel-frequency spectral (MFS) and Mel-frequency cepstral (MFC) features are studied and experimented for speech enhancement. To estimate clean speech waveform from a noisy signal, an inversion from the Mel-frequency domain to the spectral domain is required which introduces distortion artifacts in the spectrum estimation and the filtering. To reduce the corrupting effects of the inversion, the PCS modeling is proposed. This method performs concurrent modeling in both cepstral and magnitude spectral... 

In this paper a novel method, called PC-PMC, is proposed to improve the performance of automatic speech recognition systems in noisy environments. This method is based on the parallel model combination (PMC) technique and uses the Cepstral Mean Subtraction (CMS) normalization ability and Principal Component Analysis (PCA) compression and decorrelation capabilities. It takes the advantages of both additive noise compensation of PMC and convolutive noise removal ability of CMS and PCA. The first problem to be solved in the realizing of PC-PMC is that PMC algorithm requires invertible modules in the front-end of the system while CMS normalization is not an invertible process. Also, it is... 

In this paper we present an adaptive method for image compression that is based on complexity level of the image. The basic compressor/de-compressor structure of this method is a multilayer perceptron artificial neural network. In adaptive approach different Back-Propagation artificial neural networks are used as compressor and de-compressor and this is done by dividing the image into blocks, computing the complexity of each block and then selecting one network for each block according to its complexity value. Three complexity measure methods, called Entropy, Activity and Pattern-based are used to determine the level of complexity in image blocks and their ability in complexity estimation... 

In the present study, a novel theoretical model is developed, based on classical laminate theory, to predict the equivalent mechanical properties of the re-entrant lattice structures, which composed of continuous fiber reinforced composite struts. Three main mechanism of stretching, flexing and hinging are considered and a general closed-form formulation is derived to estimate the auxetic honeycomb's elastic and shear modulus as well as Poisson's ratios. In spite of previous studies in which the response of honeycomb structures is modeled using beam theory, here, each strut of unit cell is expressed as a composite laminate with orthotropic mechanical properties and classical laminate theory... 

In this paper, using of fault tolerant techniques are studied and experimented in speech recognition systems to make these systems robust to noise. Recognizer redundancy is implemented to utilize the strengths of several recognition methods that each one has acceptable performance in a specific condition. Duplication-with-comparison and NMR methods are experimented with majority and plurality voting on a telephony Persian speech-enabled IVR system. Results of evaluations present two promising outcomes, first, it improves the performance considerably; second, it enables us to detect the outputs with low confidence. © 2008 IEEE  

In this paper VTLN speaker normalization, MLLR and MAP adaptation methods are investigated in a Persian HMM-based speaker independent large vocabulary continuous speech recognition system. Speaker and environmental noise robustness are achieved in real world applications for this system. A search-based method is used in VTLN to find speaker relative warping factors. The warping factors are applied to signal's spectrum to normalize the variation effect of VTL between speakers. In the MLLR framework, Gaussian mean and covariance transformations in global and full adaptation are experienced. In this method, regression tree based adaptation in batch-supervised fashion is used. Also the standard... 

This paper presents a complexity-based image compression method using neural networks. In this method, different multi-layer perceptron ANNs are used as compressor and de-compressor. Each image is divided into blocks, complexity of each block is computed using complexity measure methods and one network is selected for each block according to its complexity value. Three complexity measure methods, called Entropy, Activity and Pattern-based are used to determine the level of complexity in image blocks and their ability are evaluated and compared together. Selection of a network for each image block is based on its complexity value or the Best-SNR criterion. Best-SNR chooses one of the trained... 

This paper addresses the robustness problem of automatic speech recognition systems for real applications in presence of noise. PMCC algorithm is proposed for combining PMC technique with CMS method. The proposed algorithm utilizes the CMS normalization ability in PMC method to takes the advantages of these methods to compensate the effect of both additive and convolutional noises. Also, we have investigated VTLN for speaker normalization and MLLR and MAP for speaker and acoustic adaptation. Different combinations of these methods are used to achieve robustness and making the system usable in real applications. Our evaluations are done on 4 different real noisy tasks on Nevisa recognition... 

Adaptive Noise Cancellation (ANC) is an effective dual-channel technique for background noise reduction. Due to the presence of uncorrelated noise components at the two inputs in vehicular environments, ANC does not provide sufficient background noise reduction. To alleviate this problem, a complementary linear filter is added to ANC structure. Filter coefficients are determined to make the enhanced signal an MMSE estimation of speech signal. Therefore, the ANC structure is modified to a dual-channel Wiener structure. We prove that this structure is identical to the LMS type ANC which is followed by a Wiener post-filter. A new method is proposed for the noise spectrum estimation in the... 

An extension to conventional Hidden Markov Model (HMM)-based speech enhancement method is developed. An algebraic method is proposed to estimate gain of speech and noise in order to improve the quality of the estimated speech. Different pronunciations and intonations may affect speech gain. Besides, gain of noise may vary remarkably from one environment to the other one. This may lead in a mismatch between energy contour of trained models and energy contour of noisy speech signal. In this work, speech gain and noise gain are estimated based on an algebraic method simultaneously in order to match gain of noisy speech and noisy model. To carry out this procedure an extension of least square... 

Adaptive Noise Cancellers (ANCs) do not provide sufficient noise reduction in the diffuse noise fields. In this paper, a new hybrid structure is proposed as a solution to this problem. The proposed system is a combination of two subsystems, an ANC and a new multistage post-filter. The post-filter is based on linear prediction (LP) and attempts to extract speech component by using intermediate ANC signals. The system is implemented on an over-sampled DFT filterbank with different analysis and synthesis prototype filters. The experimental results using various quality measures show that the proposed system is superior to both the subband ANC and subband LP based speech enhancement systems.1 ©...