Latent Class Model with Application to Speaker Diarization

In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast to the VB method, which is based on a generative model, LCM provides a framework allowing both generative and discriminative models. The discriminative property is realized through the use of i-vector (Ivec), probabilistic linear discriminative analysis (PLDA), and a support vector machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid are introduced. In addition, three further improvements are applied to enhance its performance. 1) Adding neighbor windows to extract more speaker information for each short segment. 2) Using a hidden Markov model to avoid frequent speaker change points. 3) Using an agglomerative hierarchical cluster to do initialization and present hard and soft priors, in order to overcome the problem of initial sensitivity. Experiments on the National Institute of Standards and Technology Rich Transcription 2009 speaker diarization database, under the condition of a single distant microphone, show that the diarization error rate (DER) of the proposed methods has substantial relative improvements compared with mainstream systems. Compared to the VB method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial conditions also show that the proposed LCM-Ivec-Hybrid system has the best overall performance

CFD Analysis and Experiment Study of the Rotary Two-Stage Inverter Compressor with Vapor Injection

The offset angle of the upper and lower part of the crankshaft will affect the resistance of inspiration of high stage cylinder in the rotary two-stage inverter compressor with vapor injection, and then affect the performance. this paper presents the performance of the rotary two-stage inverter compressor with vapor injection in the bias angle of the crankshaft is calculated and compared with the experimental. The simulation results are in agreement with the experimental results. Under the operation of close vapor injection and open vapor injection, the performance of compressor can be improved 1% and 3% separately by optimize the bial angle of crankshaft.

Giant efficiency of long-range orbital torque in Co/Nb bilayers

We report unambiguously experimental evidence of a strong orbital current in Nb films with weak spin-orbit coupling via the spin-torque ferromagnetic resonance (ST-FMR) spectrum for Fe/Nb and Co/Nb bilayers. The sign change of the damping-like torque in Co/Nb demonstrates a large spin-orbit correlation and thus great efficiency of orbital torque in Co/Nb. By studying the efficiency as a function of the thickness of Nb sublayer, we reveal a long orbital diffusion length (~3.1 nm) of Nb. Further planar Hall resistance (PHE) measurements at positive and negative applying current confirm the nonlocal orbital transport in ferromagnetic-metal/Nb heterostructures

A method based on multiscale base-scale entropy and random forests for roller bearings faults diagnosis

A method based on multiscale base-scale entropy (MBSE) and random forests (RF) for roller bearings faults diagnosis is presented in this study. Firstly, the roller bearings vibration signals were decomposed into base-scale entropy (BSE), sample entropy (SE) and permutation entropy (PE) values by using MBSE, multiscale sample entropy (MSE) and multiscale permutation entropy (MPE) under different scales. Then the computation time of the MBSE/MSE/MPE methods were compared. Secondly, the entropy values of BSE, SE, and PE under different scales were regarded as the input of RF and SVM optimized by particle swarm ion (PSO) and genetic algorithm (GA) algorithms for fulfilling the fault identification, and the classification accuracy was utilized to verify the effect of the MBSE/MSE/MPE methods by using RF/PSO/GA-SVM models. Finally, the experiment result shows that the computational efficiency and classification accuracy of MBSE method are superior to MSE and MPE with RF and SVM

Iron(II)-Catalyzed Intermolecular Amino-Oxygenation of Olefins through the N−O Bond Cleavage of Functionalized Hydroxylamines

An iron-catalyzed diastereoselective intermolecular olefin amino-oxygenation reaction is reported, which proceeds via an iron-nitrenoid generated by the N− O bond cleavage of a functionalized hydroxylamine. In this reaction, a bench-stable hydroxylamine derivative is used as the amination reagent and oxidant. This method tolerates a range of synthetically valuable substrates that have been all incompatible with existing amino-oxygenation methods. It can also provide amino alcohol derivatives with regio- and stereochemical arrays complementary to known amino-oxygenation methods