Automatic detection of neurological disordered subjects voice mostly relies on parameters extracted from time-domain processing. The calculation of these parameters often requires prior pitch period estimation; which in turn depends heavily on the robustness of pitch detection algorithm. In the present work cepstraldomain processing technique which does not require pitch estimation has been ado...

In this paper, a new strategy for a fast adaptation of acoustic models is proposed for embedded speech recognition. It relies on a general GMM, which represents the whole acoustic space, associated with a set of HMM state-dependent probability functions modeled as transformations of this GMM. The work presented here takes advantage of this architecture to propose a fast and efficient way to ada...

Landmarks in speech signal are regions with abrupt spectral variations. Automated detection of these regions is important for several applications in speech processing. Performance of landmark detection using parameters extracted from predefined spectral bands generally gets limited by speaker related spectral variability. This paper presents a landmark detection technique which adapts to the a...

Asymptotic expansions are made for the distributions of the Maximum Empirical Likelihood (MEL) estimator and the Estimating Equation (EE) estimator (or the Generalized Method of Moments (GMM) in econometrics) for the coefficients of a single structural equation in a system of linear simultaneous equations, which corresponds to a reduced rank regression model. The expansions in terms of the samp...

In this paper, we describe a novel model training method for one-to-many eigenvoice conversion (EVC). One-to-many EVC is a technique for converting a specific source speaker’s voice into an arbitrary target speaker’s voice. An eigenvoice Gaussian mixture model (EVGMM) is trained in advance using multiple parallel data sets consisting of utterance-pairs of the source speaker and many pre-stored ...

This paper presents a new approach to modeling speech spectra and pitch for text-independent speaker identification using Gaussian mixture models based on multi-space probability distribution (MSD-GMM). The MSD-GMM allows us to model continuous pitch values for voiced frames and discrete symbols representing unvoiced frames in a unified framework. Spectral and pitch features are jointly modeled...

The particle size of pellets is an important parameter in steel big data, and the high density overlap rate bring a great challenge to detection. To address this problem, intelligent detection algorithm with improved watershed Gaussian mixture model (GMM) proposed. First, initial segmentation background achieved by using adaptive binary segmentation, then secondary fine combining morphological ...

Actual engineering systems will be inevitably affected by uncertain factors. Thus, the Reliability-Based Multidisciplinary Design Optimization (RBMDO) has become a hotspot for recent research and application in complex system design. The Second-Order/First-Order Mean-Value Saddlepoint Approximate (SOMVSA/FOMVSA) are two popular reliability analysis strategies that widely used RBMDO. However, SO...

The Gaussian mixture modeling (GMM) techniques are increasingly being used for both speaker identification and verification. Most of these models assume diagonal covariance matrices. Although empirically any distribution can be approximated with a diagonal GMM, a large number of mixture components are usually needed to obtain a good approximation. A consequence of using a large GMM is that its ...