Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity

This paper presents a new approach for unsupervised Spoken Term Detection with spoken queries using multiple sets of acoustic patterns automatically discovered from the target corpus. The different pattern HMM configurations(number of states per model, number of distinct models, number of Gaussians per state)form a three-dimensional model granularity space. Different sets of acoustic patterns automatically discovered on different points properly distributed over this three-dimensional space are complementary to one another, thus can jointly capture the characteristics of the spoken terms. By representing the spoken content and spoken query as sequences of acoustic patterns, a series of approaches for matching the pattern index sequences while considering the signal variations are developed. In this way, not only the on-line computation load can be reduced, but the signal distributions caused by different speakers and acoustic conditions can be reasonably taken care of. The results indicate that this approach significantly outperformed the unsupervised feature-based DTW baseline by 16.16\% in mean average precision on the TIMIT corpus.Comment: Accepted by ICASSP 201

The 125 GeV Higgs and Electroweak Phase Transition Model Classes

Recently, the ATLAS and CMS detectors have discovered a bosonic particle which, to a reasonable degree of statistical uncertainty, fits the profile of the Standard Model Higgs. One obvious implication is that models which predict a significant departure from Standard Model phenomenology, such as large exotic (e.g., invisible) Higgs decay or mixing with a hidden sector scalar, are already ruled out. This observation threatens the viability of electroweak baryogenesis, which favors, for example, a lighter Higgs and a Higgs coupled to or mixed with light scalars. To assess the broad impact of these constraints, we propose a scheme for classifying models of the electroweak phase transition and impose constraints on a class-by-class basis. We find that models, such as the MSSM, which rely on thermal loop effects are severely constrained by the measurement of a 125 GeV Higgs. Models which rely on tree-level effects from a light singlet are also restricted by invisible decay and mixing constraints. Moreover, we find that the parametric region favored by electroweak baryogenesis often coincides with an enhanced symmetry point with a distinctive phenomenological character. In particular, enhancements arising through an approximate continuous symmetry are phenomenologically disfavored, in contrast with enhancements from discrete symmetries. We also comment on the excess of diphoton events observed by ATLAS and CMS. We note that although Higgs portal models can accommodate both enhanced diphoton decay and a strongly first order electroweak phase transition, the former favors a negative Higgs portal coupling whereas the latter favors a positive one, and therefore these two constraints are at tension with one another.Comment: 35 pages, 7 figure

Safety Analysis of Level Crossing Surveillance Systems Using Fuzzy Petri Nets

[[abstract]]A backward safety analysis model that can deal with dangerous status by virtue of fuzzy theory is proposed in this paper. Fuzzy Petri Nets (FPN) is developed and applied to the safety analysis for three types of level crossing surveillance systems of Taiwan Railway Administration. The numerical results of proposed FPN models are fairly plausible.[[conferencetype]]國際[[conferencedate]]20081219~20081221[[conferencelocation]]Taipei, Taiwa

Performance of self bit synchronizers for binary overlapping signals

This research is concerned with the optimum and suboptimum ways of providing self bit synchronization for binary overlapping signals. A maximum likelihood synchronizer is derived as the optimum approach. The suboptimum ways are those employing decision-directed feedback and matched derivative filter techniques which are proposed for treating the overlapping signals. Combining the two suboptimum techniques along with the bandlimited version of the overlapping signal, a suboptimum synchronizer is derived. The performance of the above synchronizer is evaluated by Monte Carlo simulation techniques. Finally, a bit synchronizer using nonlinear filtering theory is considered. The performance of a nonlinear bit synchronizer is discussed --Abstract, page ii

Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data

It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not difficult to collect a large set of audio data for each user, it is difficult to transcribe it. However, it is now possible to automatically discover acoustic tokens from unlabeled personal data in an unsupervised way. We therefore propose a multi-task deep learning framework called a phoneme-token deep neural network (PTDNN), jointly trained from unsupervised acoustic tokens discovered from unlabeled data and very limited transcribed data for personalized acoustic modeling. We term this scenario "weakly supervised". The underlying intuition is that the high degree of similarity between the HMM states of acoustic token models and phoneme models may help them learn from each other in this multi-task learning framework. Initial experiments performed over a personalized audio data set recorded from Facebook posts demonstrated that very good improvements can be achieved in both frame accuracy and word accuracy over popularly-considered baselines such as fDLR, speaker code and lightly supervised adaptation. This approach complements existing speaker adaptation approaches and can be used jointly with such techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201