Joint Bayesian Gaussian discriminant analysis for speaker verification

State-of-the-art i-vector based speaker verification relies on variants of Probabilistic Linear Discriminant Analysis (PLDA) for discriminant analysis. We are mainly motivated by the recent work of the joint Bayesian (JB) method, which is originally proposed for discriminant analysis in face verification. We apply JB to speaker verification and make three contributions beyond the original JB. 1) In contrast to the EM iterations with approximated statistics in the original JB, the EM iterations with exact statistics are employed and give better performance. 2) We propose to do simultaneous diagonalization (SD) of the within-class and between-class covariance matrices to achieve efficient testing, which has broader application scope than the SVD-based efficient testing method in the original JB. 3) We scrutinize similarities and differences between various Gaussian PLDAs and JB, complementing the previous analysis of comparing JB only with Prince-Elder PLDA. Extensive experiments are conducted on NIST SRE10 core condition 5, empirically validating the superiority of JB with faster convergence rate and 9-13% EER reduction compared with state-of-the-art PLDA.Comment: accepted by ICASSP201

A Scale Mixture Perspective of Multiplicative Noise in Neural Networks

Corrupting the input and hidden layers of deep neural networks (DNNs) with multiplicative noise, often drawn from the Bernoulli distribution (or 'dropout'), provides regularization that has significantly contributed to deep learning's success. However, understanding how multiplicative corruptions prevent overfitting has been difficult due to the complexity of a DNN's functional form. In this paper, we show that when a Gaussian prior is placed on a DNN's weights, applying multiplicative noise induces a Gaussian scale mixture, which can be reparameterized to circumvent the problematic likelihood function. Analysis can then proceed by using a type-II maximum likelihood procedure to derive a closed-form expression revealing how regularization evolves as a function of the network's weights. Results show that multiplicative noise forces weights to become either sparse or invariant to rescaling. We find our analysis has implications for model compression as it naturally reveals a weight pruning rule that starkly contrasts with the commonly used signal-to-noise ratio (SNR). While the SNR prunes weights with large variances, seeing them as noisy, our approach recognizes their robustness and retains them. We empirically demonstrate our approach has a strong advantage over the SNR heuristic and is competitive to retraining with soft targets produced from a teacher model

Turbulence and unemployment in a job matching model

According to Ljungqvist and Sargent (1998), high European unemployment since the 1980s can be explained by a rise in economic turbulence, leading to greater numbers of unemployed workers with obsolete skills. These workers refuse new jobs due to high unemployment benefits. In this paper we reassess the turbulence-unemployment relationship using a matching model with endogenous job destruction. In our model, higher turbulence reduces the incentives of employed workers to leave their jobs. If turbulence has only a tiny effect on the skills of workers experiencing endogenous separation, then the results of Lungqvist and Sargent (1998, 2004) are reversed, and higher turbulence leads to a reduction in unemployment. Thus, changes in turbulence cannot provide an explanation for European unemployment that reconciles the incentives of both unemployed and employed workers.Skill loss, European unemployment puzzle

Thermal Dileptons from Coarse-Grained Transport as Fireball Probes at SIS Energies

Utilizing a coarse-graining method to convert hadronic transport simulations of Au+Au collisions at SIS energies into local temperature, baryon and pion densities, we compute the pertinent radiation of thermal dileptons based on an in-medium $\rho$ spectral function that describes available spectra at ultrarelativistic collision energies. In particular, we analyze how far the resulting yields and slopes of the invariant-mass spectra can probe the lifetime and temperatures of the fireball. We find that dilepton radiation sets in after the initial overlap phase of the colliding nuclei of about 7 fm/c, and lasts for about 13 fm/c. This duration closely coincides with the development of the transverse collectivity of the baryons, thus establishing a direct correlation between hadronic collective effects and thermal EM radiation, and supporting a near local equilibration of the system. This fireball "lifetime" is substantially smaller than the typical 20-30 fm/c that naive considerations of the density evolution alone would suggest. We furthermore find that the total dilepton yield radiated into the invariant-mass window of $M=0.3-0.7$ GeV/$c^{2}$, normalized to the number of charged pions, follows a relation to the lifetime found earlier in the (ultra-) relativistic regime of heavy-ion collisions, and thus corroborates the versatility of this tool. The spectral slopes of the invariant-mass spectra above the $\phi$ mass provide a thermometer of the hottest phases of the collision, and agree well with the maximal temperatures extracted from the coarse-grained hadron spectra.Comment: 9 pages, 6 figures; v2: extended discussion, matches the version accepted for publicatio