Diphthong Synthesis using the Three-Dimensional Dynamic Digital Waveguide Mesh

The human voice is a complex and nuanced instrument, and despite many years of research, no system is yet capable of producing natural-sounding synthetic speech. This affects intelligibility for some groups of listeners, in applications such as automated announcements and screen readers. Furthermore, those who require a computer to speak - due to surgery or a degenerative disease - are limited to unnatural-sounding voices that lack expressive control and may not match the user's gender, age or accent. It is evident that natural, personalised and controllable synthetic speech systems are required. A three-dimensional digital waveguide model of the vocal tract, based on magnetic resonance imaging data, is proposed here in order to address these issues. The model uses a heterogeneous digital waveguide mesh method to represent the vocal tract airway and surrounding tissues, facilitating dynamic movement and hence speech output. The accuracy of the method is validated by comparison with audio recordings of natural speech, and perceptual tests are performed which confirm that the proposed model sounds significantly more natural than simpler digital waveguide mesh vocal tract models. Control of such a model is also considered, and a proof-of-concept study is presented using a deep neural network to control the parameters of a two-dimensional vocal tract model, resulting in intelligible speech output and paving the way for extension of the control system to the proposed three-dimensional vocal tract model. Future improvements to the system are also discussed in detail. This project considers both the naturalness and control issues associated with synthetic speech and therefore represents a significant step towards improved synthetic speech for use across society

Remdesivir Inhibits SARS-CoV-2 in Human Lung Cells and Chimeric SARS-CoV Expressing the SARS-CoV-2 RNA Polymerase in Mice

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the novel viral disease COVID-19. With no approved therapies, this pandemic illustrates the urgent need for broad-spectrum antiviral countermeasures against SARS-CoV-2 and future emerging CoVs. We report that remdesivir (RDV) potently inhibits SARS-CoV-2 replication in human lung cells and primary human airway epithelial cultures (EC50 = 0.01 μM). Weaker activity is observed in Vero E6 cells (EC50 = 1.65 μM) because of their low capacity to metabolize RDV. To rapidly evaluate in vivo efficacy, we engineered a chimeric SARS-CoV encoding the viral target of RDV, the RNA-dependent RNA polymerase of SARS-CoV-2. In mice infected with the chimeric virus, therapeutic RDV administration diminishes lung viral load and improves pulmonary function compared with vehicle-treated animals. These data demonstrate that RDV is potently active against SARS-CoV-2 in vitro and in vivo, supporting its further clinical testing for treatment of COVID-19

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions