2,832 research outputs found
Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities
Voice conversion (VC) using sequence-to-sequence learning of context
posterior probabilities is proposed. Conventional VC using shared context
posterior probabilities predicts target speech parameters from the context
posterior probabilities estimated from the source speech parameters. Although
conventional VC can be built from non-parallel data, it is difficult to convert
speaker individuality such as phonetic property and speaking rate contained in
the posterior probabilities because the source posterior probabilities are
directly used for predicting target speech parameters. In this work, we assume
that the training data partly include parallel speech data and propose
sequence-to-sequence learning between the source and target posterior
probabilities. The conversion models perform non-linear and variable-length
transformation from the source probability sequence to the target one. Further,
we propose a joint training algorithm for the modules. In contrast to
conventional VC, which separately trains the speech recognition that estimates
posterior probabilities and the speech synthesis that predicts target speech
parameters, our proposed method jointly trains these modules along with the
proposed probability conversion modules. Experimental results demonstrate that
our approach outperforms the conventional VC.Comment: Accepted to INTERSPEECH 201
Evaluation of the three-dimensional configuration of a subsurface artificial fracture by the triaxial shear shadow method
Dynamical Casimir effect for magnons in a spinor Bose-Einstein condensate
Magnon excitation in a spinor Bose-Einstein condensate by a driven magnetic
field is shown to have a close analogy with the dynamical Casimir effect. A
time-dependent external magnetic field amplifies quantum fluctuations in the
magnetic ground state of the condensate, leading to magnetization of the
system. The magnetization occurs in a direction perpendicular to the magnetic
field breaking the rotation symmetry. This phenomenon is numerically
demonstrated and the excited quantum field is shown to be squeezed.Comment: 8 pages, 3 figure
Online Automated Micro Sample Preparation for High-Performance Liquid Chromatography
Sample preparation is one of the most labor-intensive and time-consuming operations in sample analysis. Sample preparation strategies include the exhaustive or non-exhaustive extraction of analytes from matrices. Online coupling of sample preparation with the separation system is regarded as an important goal. In-tube solid-phase microextraction (SPME) is an effective sample preparation technique that uses an open tubular fused-silica capillary column as an extraction device. In-tube SPME is useful for trace enrichment, automated sample cleanup, and rapid online analysis. Moreover, this method can be used to determine the analytes in complex matrices by direct sample injection or merely by simple sample treatment such as filtration. In-tube SPME is frequently combined with high-performance liquid chromatography (HPLC) using online column-switching techniques. Various operating systems and new sorbent materials have been reported to improve extraction efficiency, such as sorption capacity and selectivity. This chapter discusses efficient micro sample preparation techniques for HPLC, especially online automated in-tube SPME
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