14,973 research outputs found
Cross-Lingual Speaker Discrimination Using Natural and Synthetic Speech
This paper describes speaker discrimination experiments in which native English listeners were presented with either natural speech stimuli in English and Mandarin, synthetic speech stimuli in English and Mandarin, or natural Mandarin speech and synthetic English speech stimuli. In each experiment, listeners were asked to decide whether they thought the sentences were spoken by the same person or not. We found that the results for Mandarin/English speaker discrimination are very similar to results found in previous work on German/English and Finnish/English speaker discrimination. We conclude from this and previous work that listeners are able to identify speakers across languages and they are able to identify speakers across speech types, but the combination of these two factors leads to a speaker discrimination task which is too difficult for listeners to perform successfully, given the quality of across-language speaker adapted speech synthesis at present. Index Terms: speaker discrimination, speaker adaptation, HMM-based speech synthesi
Recurrent 3D Pose Sequence Machines
3D human articulated pose recovery from monocular image sequences is very
challenging due to the diverse appearances, viewpoints, occlusions, and also
the human 3D pose is inherently ambiguous from the monocular imagery. It is
thus critical to exploit rich spatial and temporal long-range dependencies
among body joints for accurate 3D pose sequence prediction. Existing approaches
usually manually design some elaborate prior terms and human body kinematic
constraints for capturing structures, which are often insufficient to exploit
all intrinsic structures and not scalable for all scenarios. In contrast, this
paper presents a Recurrent 3D Pose Sequence Machine(RPSM) to automatically
learn the image-dependent structural constraint and sequence-dependent temporal
context by using a multi-stage sequential refinement. At each stage, our RPSM
is composed of three modules to predict the 3D pose sequences based on the
previously learned 2D pose representations and 3D poses: (i) a 2D pose module
extracting the image-dependent pose representations, (ii) a 3D pose recurrent
module regressing 3D poses and (iii) a feature adaption module serving as a
bridge between module (i) and (ii) to enable the representation transformation
from 2D to 3D domain. These three modules are then assembled into a sequential
prediction framework to refine the predicted poses with multiple recurrent
stages. Extensive evaluations on the Human3.6M dataset and HumanEva-I dataset
show that our RPSM outperforms all state-of-the-art approaches for 3D pose
estimation.Comment: Published in CVPR 201
Some aspects of global Lambda polarization in heavy-ion collisions
Large orbital angular momentum can be generated in non-central heavy-ion
collisions, and part of it is expected to be converted into final particle's
polarization due to the spin-orbit coupling. Within the framework of A
Multi-Phase Transport (AMPT) model, we studied the vorticity-induced
polarization of hyperons at the midrapidity region in
Au-Au collisions at energies GeV. Our results show
that the global polarization decreases with the collisional energies and is
consistent with the recent STAR measurements. This behavior can be understood
by less asymmetry of participant matter in the midrapidity region due to faster
expansion of fireball at higher energies. As another evidence, we discuss how
much the angular momentum is deposited in different rapidity region. The result
supports our asymmetry argument.Comment: 6 pages, 4 figures, CPOD 2017 proceedin
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