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

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

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