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

Mycorrhiza and Common Mycorrhizal Network Regulate the Production of Signal Substances in Trifoliate Orange (Poncirus trifoliata)

Common mycorrhizal networks (CMNs) connecting two or more neighbouring plants are confirmed to transfer signals, whereas little information about CMNs effects on the signal substances production is known. In this study, a two-chambered rootbox separated by 37 µm nylon mesh was used to establish donor and receptor chambers. Two chambers both were planted with trifoliate orange (Poncirus trifoliata) and then only donor chamber inoculated with Diversispora versiformis, Paraglomus occultum and Rhizoglomus intraradices. The roots of the donor and receptor plants both were mycorrhizated suggesting that CMNs were established between donor and receptor seedlings. Moreover, the AMF association dramatically increased plant height, stem diameter, leaf numbers, and shoot and root biomass in both the donor and receptor seedlings. The AMF inoculation in the donor plants and the subsequent mycorrhizal colonization by CMNs in the receptor plants significantly increased root calmodulin (CaM) and salicylic acid (SA) concentrations, while considerably decreased root nitric oxide (NO) and jasmonic acid (JA) concentrations. This was accompanied by down-regulated expression of three JA synthetic genes (PtLOX, PtAOS and PtAOC), regardless of donor and receptor seedlings. These results thus suggest that CMNs between trifoliate orange seedlings manifestly promote plant growth and affect the production of signal substances

The Effect of Off-Farm Employment on Forestland Transfers in China : A Simultaneous-Equation Tobit Model Estimation

China's new round tenure reform has devolved collective forests to individuals on an egalitarian basis. To balance the equity-efficiency dilemma, forestland transfers are highly advocated by policymakers. However, the forestland rental market is still inactive after the reform. To examine the role of off-farm employment in forestland transfers, a simultaneous Tobit system of equations was employed to account for the endogeneity, interdependency, and censoring issues. Accordingly, the Nelson-Olson two-stage procedure, embedded with a multivariate Tobit estimator, was applied to a nationally representative dataset. The estimation results showed that off-farm employment plays a significantly negative role in forestland rent-in, at the 5% risk level. However, off-farm activities had no significant effect on forestland rent-out. Considering China's specific situation, a reasonable explanation is that households hold forestland as a crucial means of social security against the risk of unemployment. In both rent-in and rent-out equations, high transaction costs are one of the main obstacles impeding forestland transfer. A remarkable finding was that forestland transactions occurred with a statistically significant factor equalization effect, which would be helpful to adjust the mismatched labor-land ratio and improve the land-use efficiency.Peer reviewe

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