Noise adaptive training for subspace Gaussian mixture models

Noise adaptive training (NAT) is an effective approach to normalise the environmental distortions in the training data. This paper investigates the model-based NAT scheme using joint uncertainty decoding (JUD) for subspace Gaussian mixture models (SGMMs). A typical SGMM acoustic model has much larger number of surface Gaussian components, which makes it computationally infeasible to compensate each Gaussian explicitly. JUD tackles the problem by sharing the compensation parameters among the Gaussians and hence reduces the computational and memory demands. For noise adaptive training, JUD is reformulated into a generative model, which leads to an efficient expectation-maximisation (EM) based algorithm to update the SGMM acoustic model parameters. We evaluated the SGMMs with NAT on the Aurora 4 database, and obtained higher recognition accuracy compared to systems without adaptive training. Index Terms: adaptive training, noise robustness, joint uncertainty decoding, subspace Gaussian mixture model

Joint Uncertainty Decoding with Unscented Transform for Noise Robust Subspace Gaussian Mixture Models

Common noise compensation techniques use vector Taylor series (VTS) to approximate the mismatch function. Recent work shows that the approximation accuracy may be improved by sampling. One such sampling technique is the unscented transform (UT), which draws samples deterministically from clean speech and noise model to derive the noise corrupted speech parameters. This paper applies UT to noise compensation of the subspace Gaussian mixture model (SGMM). Since UT requires relatively smaller number of samples for accurate estimation, it has significantly lower computational cost compared to other random sampling techniques. However, the number of surface Gaussians in an SGMM is typically very large, making the direct application of UT, for compensating individual Gaussian components, computationally impractical. In this paper, we avoid the computational burden by employing UT in the framework of joint uncertainty decoding (JUD), which groups all the Gaussian components into small number of classes, sharing the compensation parameters by class. We evaluate the JUD-UT technique for an SGMM system using the Aurora 4 corpus. Experimental results indicate that UT can lead to increased accuracy compared to VTS approximation if the JUD phase factor is untuned, and to similar accuracy if the phase factor is tuned empirically. 1

Multiform Adaptive Robot Skill Learning from Humans

Object manipulation is a basic element in everyday human lives. Robotic manipulation has progressed from maneuvering single-rigid-body objects with firm grasping to maneuvering soft objects and handling contact-rich actions. Meanwhile, technologies such as robot learning from demonstration have enabled humans to intuitively train robots. This paper discusses a new level of robotic learning-based manipulation. In contrast to the single form of learning from demonstration, we propose a multiform learning approach that integrates additional forms of skill acquisition, including adaptive learning from definition and evaluation. Moreover, going beyond state-of-the-art technologies of handling purely rigid or soft objects in a pseudo-static manner, our work allows robots to learn to handle partly rigid partly soft objects with time-critical skills and sophisticated contact control. Such capability of robotic manipulation offers a variety of new possibilities in human-robot interaction.Comment: Accepted to 2017 Dynamic Systems and Control Conference (DSCC), Tysons Corner, VA, October 11-1

Effects of Halogen, Chalcogen, Pnicogen, and Tetrel Bonds on IR and NMR Spectra

Complexes were formed pairing FX, FHY, FH2Z, and FH3T (X = Cl, Br, I; Y = S, Se, Te; Z = P, As, Sb; T = Si, Ge, Sn) with NH3 in order to form an A⋯N noncovalent bond, where A refers to the central atom. Geometries, energetics, atomic charges, and spectroscopic characteristics of these complexes were evaluated via DFT calculations. In all cases, the A–F bond, which is located opposite the base and is responsible for the σ-hole on the A atom, elongates and its stretching frequency undergoes a shift to the red. This shift varies from 42 to 175 cm−1 and is largest for the halogen bonds, followed by chalcogen, tetrel, and then pnicogen. The shift also decreases as the central A atom is enlarged. The NMR chemical shielding of the A atom is increased while that of the F and electron donor N atom are lowered. Unlike the IR frequency shifts, it is the third-row A atoms that undergo the largest change in NMR shielding. The change in shielding of A is highly variable, ranging from negligible for FSnH3 all the way up to 1675 ppm for FBr, while those of the F atom lie in the 55–422 ppm range. Although smaller in magnitude, the changes in the N shielding are still easily detectable, between 7 and 27 ppm

Comparison of Halogen with Proton Transfer. Symmetric and Asymmetric Systems

The transfer of the halogen atom X within (A⸳⸳X⸳⸳A)+ systems was calculated for A = NH3, OH2, and CH3, and where X=Cl, Br, and I. These potentials are similar to those computed for equivalent proton transfers. Each contains a single symmetric well for short R(A⸳⸳A) distances. As R is stretched a second minimum appears, separated from the first by a transfer barrier E† which climbs quickly as R is elongated. The central X prefers association with the N in asymmetric systems (H3NX⸳⸳OH2)+, but a second (H3N⸳⸳XOH2)+ minimum, albeit less stable than the first, can appear if R(N⸳⸳O) is stretched