Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks

Investigating NMF Speech Enhancement for Neural Network based Acoustic Models

In the light of the improvements that were made in the last years with neural network-based acoustic models, it is an interesting question whether these models are also suited for noise-robust recognition. This has not yet been fully explored, although first experiments confirm this question. Furthermore, preprocessing techniques that improve the robustness should be re-evaluated with these new models. In this work, we present experimental results to address these questions. Acoustic models based on Gaussian mixture models (GMMs), deep neural networks (DNNs), and long short-term memory (LSTM) recurrent neural networks (which have an improved ability to exploit context) are evaluated for their robustness after clean or multi-condition training. In addition, the influence of non-negative matrix factorization (NMF) for speech enhancement is investigated. Experiments are performed with the Aurora-4 database and the results show that DNNs perform slightly better than LSTMs and, as expected, both beat GMMs. Furthermore, speech enhancement is capable of improving the DNN result. Index Terms: robust speech recognition, long short-term memory, speech enhancemen

A comprehensive evaluation of the activity and selectivity profile of ligands for RGD-binding integrins

Integrins, a diverse class of heterodimeric cell surface receptors, are key regulators of cell structure and behaviour, affecting cell morphology, proliferation, survival and differentiation. Consequently, mutations in specific integrins, or their deregulated expression, are associated with a variety of diseases. In the last decades, many integrin-specific ligands have been developed and used for modulation of integrin function in medical as well as biophysical studies. The IC50-values reported for these ligands strongly vary and are measured using different cell-based and cell-free systems. A systematic comparison of these values is of high importance for selecting the optimal ligands for given applications. In this study, we evaluate a wide range of ligands for their binding affinity towards the RGD-binding integrins avß3, avß5, avß6, avß8, a5ß1, aIIbß3, using homogenous ELISA-like solid phase binding assay.Postprint (published version

Reversible Copper Sulfide Conversion in Nonflammable Trimethyl Phosphate Electrolytes for Safe Sodium‐Ion Batteries

Rechargeable sodium-ion batteries are considered promising candidates for low-cost and large-scale energy storage systems. However, the limited energy density, cyclability, and safety issues remain challenges for practical applications. Herein, investigation of the Cu1.8S/C composite material as the negative electrode active (conversion) material in combination with a concentrated electrolyte composed of a 3.3 m solution of sodium bis(fluorosulfonyl)imide (NaFSI) in trymethyl phosphate and fluoroethylene carbonate (FEC) as the additive is reported on. Such a combination enables the stable cycling of the conversion-type Cu1.8S/C electrode material for hundreds of cycles with high capacity (380 mAh g−1). Both the salt (NaFSI) and the additive (FEC) contribute to the formation of a stable NaF-rich solid electrolyte interphase (SEI) on the anode surface. A full cell using the Na3V2(PO4)3/C cathode also demonstrates stable cycling performance for 200 cycles with a promising Coulombic efficiency (CE) (99.3%). These findings open new opportunities for the development of safer rechargeable sodium-ion batteries

Superior Lithium Storage Capacity of α‐MnS Nanoparticles Embedded in S‐Doped Carbonaceous Mesoporous Frameworks

Herein, a Mn‐based metal–organic framework is used as a precursor to obtain well‐defined α‐MnS/S‐doped C microrod composites. Ultrasmall α‐MnS nanoparticles (3–5 nm) uniformly embedded in S‐doped carbonaceous mesoporous frameworks (α‐MnS/SCMFs) are obtained in a simple sulfidation reaction. As‐obtained α‐MnS/SCMFs shows outstanding lithium storage performance, with a specific capacity of 1383 mAh g−1 in the 300th cycle or 1500 mAh g−1 in the 120th cycle (at 200 mA g−1) using copper or nickel foil as the current collector, respectively. The significant (pseudo)capacitive contribution and the stable composite structure of the electrodes result in impressive rate capabilities and outstanding long‐term cycling stability. Importantly, in situ X‐ray diffraction measurements studies on electrodes employing various metal foils/disks as current collector reveal the occurrence of the conversion reaction of CuS at (de)lithiation process when using copper foil as the current collector. This constitutes the first report of the reaction mechanism for α‐MnS, eventually forming metallic Mn and Li2S. In situ dilatometry measurements demonstrate that the peculiar structure of α‐MnS/SCMFs effectively restrains the electrode volume variation upon repeated (dis)charge processes. Finally, α‐MnS/SCMFs electrodes present an impressive performance when coupled in a full cell with commercial LiMn1/3Co1/3Ni1/3O2 cathodes

Impact of the Transition Metal Dopant in Zinc Oxide Lithium-Ion Anodes on the Solid Electrolyte Interphase Formation

Conversion/alloying materials (CAMs) provide substantially higher specific capacities than graphite, the state‐of‐the‐art lithium‐ion battery anode material. The ability to host much more lithium per unit weight and volume is, however, accompanied by significant volume changes, which challenges the realization of a stable solid electrolyte interphase (SEI). Herein, the comprehensive characterization of the composition and evolution of the SEI on transition metal (TM) doped zinc oxide as CAM model compound, is reported, with a particular focus on the impact of the TM dopant (Fe or Co). The results unveil that the presence of iron specifically triggers the electrolyte decomposition. However, this detrimental effect can be avoided by stabilizing the interface with the electrolyte by a carbonaceous coating. These findings provide a great leap forward toward the enhanced understanding of such doped materials and (transition) metal oxide active materials in general

Enhanced Electrochemical Capacity of Spherical Co-Free Li1.2_{1.2}Mn0.6_{0.6}Ni0.2_{0.2}O2_{2} Particles after a Water and Acid Treatment and its Influence on the Initial Gas Evolution Behavior

Li-rich layered oxides (LRLO) with specific energies beyond 900 Wh kg$^{−1}$ are one promising class of high-energy cathode materials. Their high Mn-content allows reducing both costs and the environmental footprint. In this work, Co-free Li$_{1.2}$Mn$_{0.6}$Ni$_{0.2}$O$_{2}$ was investigated. A simple water and acid treatment step followed by a thermal treatment was applied to the LRLO to reduce surface impurities and to establish an artificial cathode electrolyte interface. Samples treated at 300 °C show an improved cycling behavior with specific first cycle capacities of up to 272 mAh g$^{−1}$, whereas powders treated at 900 °C were electrochemically deactivated due to major structural changes of the active compounds. Surface sensitive analytical methods were used to characterize the structural and chemical changes compared to the bulk material. Online DEMS measurements were conducted to get a deeper understanding of the effect of the treatment strategy on O$_2$ and CO$_2$ evolution during electrochemical cycling