The 2010 Silent Speech Challenge benchmark is updated with new results obtained in a Deep Learning strategy, using the same input features and decoding strategy as in the original article. A Word Error Rate of 6.4% is obtained, compared to the published value of 17.4%. Additional results comparing new auto-encoder-based features with the original features at reduced dimensionality, as well as decoding scenarios on two different language models, are also presented. The Silent Speech Challenge archive has been updated to contain both the original and the new auto-encoder features, in addition to the original raw data. Index Terms-silent speech interface, multimodal speech recognition, deep learning, language model INTRODUCTION 1.1.Silent speech interfaces and challengesA Silent Speech Interface, or SSI, is defined as a device enabling speech processing in the absence of an exploitable audio signal -for example, speech recognition obtained exclusively from video images of the mouth, or from electromyographic sensors (EMA) glued to the tongue. Classic applications targeted by SSIs include: 1)Voice-replacement for persons who have lost the ability to vocalize through illness or an accident, yet who retain the ability to articulate;2) Speech communication in environments where silence is either necessary or desired: responding to cellphone in meetings or public places without disturbing others; avoiding interference in call centers, conferences and classrooms; private communications by police, military, or business personnel. 7)Cortical implants for a "thought-driven" SSI. Figure 1: Overview of an SSI, showing non-acoustic sensors and non-acoustic automatic speech recognition, ASR, which can be followed by speech synthesis, or retained as a phonetic, text, or other digital representation, depending on the application.As a non-acoustic technology, SSIs initially stood somewhat apart from the main body of speech processing, where the standard techniques are intrinsically associated with an audio signal. Nevertheless, the novelty of the SSI concept and their exciting range of applications -perhaps aided by an accrued interest in multi-modal speech processing -are gradually allowing SSI technology to join the speech processing main stream. Activity in SSI research has remained strong since the publication of [1], which received the ISCA/Eurasip Best Paper Award in 2015. A recent survey of the literature reveals dozens of publications on SSI systems, using not only on the original seven non-acoustic technologies mentioned above, but also two additional ones, namely, low frequency air-borne ultrasound; and micropower radar .Despite this activity, SSIs today remain for the most part specialized laboratory instruments. The performance of any automatic speech recognition (ASR) system is most often characterized by a WordError Rate, or WER, expressed as a percentage of the total number of words appearing in a corpus. To date, no SSI ASR system has been able to achieve WER parity with state-of-the-art acoustic ASR.Indeed, a numbe...

Zinc is an essential metal in bacteria. One important bacterial zinc transporter is AdcA, and most bacteria possess AdcA homologs that are single-domain small proteins due to better efficiency of protein biogenesis. However, a double-domain AdcA with two zinc-binding sites is significantly overrepresented in species, many of which are major human pathogens. Using molecular simulation and experimental validations of AdcA from, we found here that the two AdcA domains sequentially stabilize the structure upon zinc binding, indicating an organization required for both increased zinc affinity and transfer speed. This structural organization appears to endow species with distinct advantages in zinc-depleted environments, which would not be achieved by each single AdcA domain alone. This enhanced zinc transport mechanism sheds light on the significance of the evolution of the AdcA domain fusion, provides new insights into double-domain transporter proteins with two binding sites for the same ion, and indicates a potential target of antimicrobial drugs against pathogenic species.

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The interaction between vincamine (VCM) and human serum albumin (HSA) has been studied using a fluorescence quenching technique in combination with UV/vis absorption spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, circular dichroism (CD) spectroscopy and molecular modeling under conditions similar to human physiological conditions. VCM effectively quenched the intrinsic fluorescence of HSA via static quenching. The binding constants were calculated from the fluorescence data. Thermodynamic analysis by Van't Hoff equation revealed enthalpy change (ΔH) and entropy change (ΔS) were -4.57 kJ/mol and 76.26 J/mol/K, respectively, which indicated that the binding process was spontaneous and the hydrophobic interaction was the predominant force. The distance r between the donor (HSA) and acceptor (VCM) was obtained according to the Förster's theory of non-radiative energy transfer and found to be 4.41 nm. Metal ions, viz., Na(+), K(+), Li(+), Ni(2+), Ca(2+), Zn(2+) and Al(3+) were found to influence binding of the drug to protein. The 3D fluorescence, FT-IR and CD spectral results revealed changes in the secondary structure of the protein upon interaction with VCM. Furthermore, molecular modeling indicated that VCM could bind to the subdomain IIA (site I) of HSA.

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