DENT-DDSP: Data-efficient noisy speech generator using differentiable digital signal processors for explicit distortion modelling and noise-robust speech recognition

The performances of automatic speech recognition (ASR) systems degrade drastically under noisy conditions. Explicit distortion modelling (EDM), as a feature compensation step, is able to enhance ASR systems under such conditions by simulating the in-domain noisy speeches from the clean counterparts. Yet, existing distortion models are either non-trainable or unexplainable and often lack controllability and generalization ability. In this paper, we propose a fully explainable and controllable model: DENT-DDSP to achieve EDM. DENT-DDSP utilizes novel differentiable digital signal processing (DDSP) components and requires only 10 seconds of training data to achieve high fidelity. The experiment shows that the simulated noisy data from DENT-DDSP achieves the highest simulation fidelity compared to other baseline models in terms of multi-scale spectral loss (MSSL). Moreover, to validate whether the data simulated by DENT-DDSP are able to replace the scarce in-domain noisy data in the noise-robust ASR tasks, several downstream ASR models with the same architecture are trained using the simulated data and the real data. The experiment shows that the model trained with the simulated noisy data from DENT-DDSP achieves similar performances to the benchmark with a 2.7\% difference in terms of word error rate (WER). The code of the model is released online

Influence of Tutor Behaviours on the Process of Problem-Based Learning

__Abstract__ The theme of this thesis revolves around the behaviours of the tutor in problem-based learning (PBL) and its effects on the learning in this approach. Although a substantial amount of research on PBL has been conducted over the years, it is still relatively unclear how learning takes place during the PBL process. In addition, factors that influence the learning process such as the quality of problems, the tutor and the use of scaffolds are areas that require greater investigation (Schmidt, Rotgans and Yew, 2011). With these considerations in mind, the research conducted in this thesis aims to deepen the understanding of what occurs during the actual learning process of PBL and in particular, the impact PBL tutors have on student learning

Compartment Syndrome following Intramedullary Nail Fixation in Closed Tibial Shaft Fractures

Introduction: Compartment syndrome complicating intramedullary nailing of closed tibia fractures has been described as early as the 1980s, but currently remains less described in literature compared to compartment syndrome directly following trauma. This study aims to review this potentially disabling complication and highlight the importance of timely diagnosis and management of compartment syndrome following fracture fixation, not just after fracture itself, via a review of three cases. Material and methods: A retrospective study of a series of three cases was conducted. The type of fracture, wait time to fixation, surgery duration, reaming, size of nail implant used, tourniquet time, and surgical technique were recorded. Time to diagnosis of compartment syndrome, compartment pressure if available, extent of muscle necrosis, reconstructive procedures performed, and post-operative complications were analysed. Results: The three cases following high-energy trauma from road traffic accidents presented from January to May 2010. Compartment syndrome was diagnosed clinically for all cases, between one to six days post-operatively and supported by elevated compartment pressure measurements in two of the three cases. Conclusion: This study advocates thorough clinical monitoring and maintaining strong clinical suspicion of compartment syndrome in patients even after intramedullary nail fixation of tibial shaft fractures to achieve timely limb- salvaging intervention. While intercompartmental pressure can be used to aid in diagnosis, we do not advise using it in isolation to diagnose compartment syndrome. Tendon transfer improves functional mobility and provides a good result in patients with severe muscle damage, while skin grafting sufficient in patients with minimal muscle damag

Text-Independent F0 Transformation with Non-Parallel Data for Voice Conversion

In voice conversion, frame-level mean and variance normalization is typically used for fundamental frequency (F0) transformation, which is text-independent and requires no parallel training data. Some advanced methods transform pitch contours instead, but require either parallel training data or syllabic annotations. We propose a method which retains the simplicity and text-independence of the frame-level conversion while yielding high-quality conversion. We achieve these goals by (1) introducing a text-independent tri-frame alignment method, (2) including delta features of F0 into Gaussian mixture model (GMM) conversion and (3) reducing the well-known GMM oversmoothing effect by F0 histogram equalization. Our objective and subjective experiments on the CMU Arctic corpus indicate improvements over both the mean/variance normalization and the baseline GMM conversion

Spoofing detection from a feature representation perspective

Spoofing detection, which discriminates the spoofed speech from the natural speech, has gained much attention recently. Low-dimensional features that are used in speaker recognition/verification are also used in spoofing detection. Unfortunately, they don't capture sufficient information required for spoofing detection. In this work, we investigate the use of high-dimensional features for spoofing detection, that maybe more sensitive to the artifacts in the spoofed speech. Six types of high-dimensional feature are employed. For each kind of feature, four different representations are extracted, i.e. the original high-dimensional feature, corresponding low-dimensional feature, the low- and the high-frequency regions of the original high-dimensional feature. Dynamic features are also calculated to assess the effectiveness of the temporal information to detect the artifacts across frames. A neural network-based classifier is adopted to handle the high-dimensional features. Experimental results on the standard ASVspoof 2015 corpus suggest that high-dimensional features and dynamic features are useful for spoofing attack detection. A fusion of them has been shown to achieve 0.0% the equal error rates for nine of ten attack types.NRF (Natl Research Foundation, S’pore)Accepted versio

Navigating SEL From the Inside Out: Looking Inside & Across 18 Leading SEL Programs: A Practical Resource for Schools and OST ProvidersMiddle & High School Focus

The field of social and emotional learning (SEL) is rapidly expanding, as evidence emerges that social and emotional skills have a positive impact on learning and life outcomes. This guide to evidence-based SEL programs provides detailed information on 18 middle and high school programs, encompassing curricular content and program highlights. School or out-of-school-time program practitioners interested in SEL can use the resource to look "inside and across" SEL programs to better understand their content and assess their fit with school district or community needs.?The guide was written by Harvard Graduate School of Education professor Stephanie Jones, an expert in social and emotional learning, and a team of researchers. It is a practical resource that provides profiles of each program, including the specific skills targeted and instructional methods used. Some programs, for example, are designed to help students regulate their behavior and build positive relationships, while others are aimed at developing certain mindsets or character traits.Much of the guide focuses on detailed program information, while introductory chapters discuss a range of topics, including SEL in out-of-school-time (OST) programming, equitable SEL and a trauma-sensitive approach to SEL.In addition to helping schools and OST providers make decisions about choosing a social and emotional learning program, the guide is designed to be a useful resource for those who want to better understand social and emotional learning and the landscape of available programs or assess the effectiveness of one they are already using. A supplement includes worksheets to help users select a program and think through considerations on everything from program components to program duration and cost.??Key components of the guide include: Background information on SEL and its benefits, including key features of effective programs and common implementation challenges, A summary of the evidence base for each of the 18 programs, Recommendations for adapting the programs to OST settings, Summary tables that allow users to compare unique features, program components, and instructional methods, as well as see which skills each program targets, and Detailed individual profiles for each of the programs

Estimation of PM10 from exhaust and non-exhaust emission in traffic area, Klang Valley, Peninsular Malaysia using air quality dispersion modeling

This paper reviews methods to estimate the concentration of PM10 from vehicular emission sources (exhaust and non-exhaust) by using AERMOD dispersion model in the Klang Valley region. The ground level concentration was obtained by processing various meteorological parameters, terrain features and source emission inventory data (emission rate) for 2014 were used in simulations within 50 km x 50 km model domain over 24 hours averaging periods. The results showed the maximum concentrations of PM10 were revealed in central, southeast and southwest of the model domain. The evaluation of performance of the model was done by comparing observed and simulated PM10 concentrations using statistical tools such as correlation coefficient, Normalized Mean Square Error, Factor of two and index of agreement. Therefore, the AERMOD model evaluation results revealed an acceptable model for conducting dispersion modeling from vehicular sources (exhaust and non-exhaust) in the Klang Valley with good model skill for the estimation of PM10 concentrations in Shah Alam station. This study considers the first for evaluation PM10 using AERMOD dispersion model in the Klang Valley region in Peninsular Malaysia

Generalized Weyl solutions in d=5 Einstein-Gauss-Bonnet theory: the static black ring

We argue that the Weyl coordinates and the rod-structure employed to construct static axisymmetric solutions in higher dimensional Einstein gravity can be generalized to the Einstein-Gauss-Bonnet theory. As a concrete application of the general formalism, we present numerical evidence for the existence of static black ring solutions in Einstein-Gauss-Bonnet theory in five spacetime dimensions. They approach asymptotically the Minkowski background and are supported against collapse by a conical singularity in the form of a disk. An interesting feature of these solutions is that the Gauss-Bonnet term reduces the conical excess of the static black rings. Analogous to the Einstein-Gauss-Bonnet black strings, for a given mass the static black rings exist up to a maximal value of the Gauss-Bonnet coupling constant $\alpha'$. Moreover, in the limit of large ring radius, the suitably rescaled black ring maximal value of $\alpha'$ and the black string maximal value of $\alpha'$ agree.Comment: 43 pages, 14 figure