Feature learning with raw-waveform CLDNNs for Voice Activity Detection

Voice Activity Detection (VAD) is an important preprocessing step in any state-of-the-art speech recognition system. Choosing the right set of features and model architecture can be challenging and is an active area of research. In this paper we propose a novel approach to VAD to tackle both feature and model selection jointly. The proposed method is based on a CLDNN (Convolutional, Long Short-Term Memory, Deep Neural Networks) architecture fed directly with the raw waveform. We show that using the raw waveform allows the neural network to learn features directly for the task at hand, which is more powerful than using log-mel features, specially for noisy environments. In addition, using a CLDNN, which takes advantage of both frequency modeling with the CNN and temporal modeling with LSTM, is a much better model for VAD compared to the DNN. The proposed system achieves over 78% relative improvement in False Alarms (FA) at the operating point of 2% False Rejects (FR) on both clean and noisy conditions compared to a DNN of comparable size trained with log-mel features. In addition, we study the impact of the model size and the learned features to provide a better understanding of the proposed architecture

Network strategies to understand the aging process and help age-related drug design

Recent studies have demonstrated that network approaches are highly appropriate tools to understand the extreme complexity of the aging process. The generality of the network concept helps to define and study the aging of technological, social networks and ecosystems, which may give novel concepts to cure age-related diseases. The current review focuses on the role of protein-protein interaction networks (interactomes) in aging. Hubs and inter-modular elements of both interactomes and signaling networks are key regulators of the aging process. Aging induces an increase in the permeability of several cellular compartments, such as the cell nucleus, introducing gross changes in the representation of network structures. The large overlap between aging genes and genes of age-related major diseases makes drugs which aid healthy aging promising candidates for the prevention and treatment of age-related diseases, such as cancer, atherosclerosis, diabetes and neurodegenerative disorders. We also discuss a number of possible research options to further explore the potential of the network concept in this important field, and show that multi-target drugs (representing "magic-buckshots" instead of the traditional "magic bullets") may become an especially useful class of age-related future drugs.Comment: an invited paper to Genome Medicine with 8 pages, 2 figures, 1 table and 46 reference

Average game centrality (GC) values for <i>E. coli</i> methionyl-tRNA synthetase amino acids.

a<p>Protein structure network of <i>E coli</i> methionyl-tRNA-synthetase was constructed, Prisoner’s dilemma game was simulated, and game centrality measures were calculated as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067159#s4" target="_blank">Methods</a>.</p>b<p>Domains from top to bottom: the catalytic domain including the Rossmann-fold-1 (catalytic function), Rossmann-fold-2 and stem contact fold (KMSKS) sub-domains; the connecting peptide (CP) domain; the anticodon binding, carboxy-terminal domain, 43 signaling amino acids involved in the transmission of conformational change as shown by Ghosh and Vishveshwara <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067159#pone.0067159-Ghosh2" target="_blank">[46]</a>, whole methionyl-tRNA synthetase.</p

Correlations of game centrality (GC) with degree, betweenness centrality and phenotypic potential of proteins in a high fidelity yeast interactome.

a<p>Simulations of the prisoner’s dilemma game were performed as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067159#s4" target="_blank">Methods</a> using the parameter set of (R = 3, T = 6, S = 0, P = 1). Correlation values between degree, betweenness centrality, GC in prisoner’s dilemma game, as well as phenotypic potential <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067159#pone.0067159-Levy1" target="_blank">[44]</a> were calculated for the 2,444 proteins of the high fidelity yeast interactome of Ekman <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067159#pone.0067159-Ekman1" target="_blank">[36]</a>.</p>b<p>Data represent Goodman-Kruskal’s gamma values ± standard errors. Significance levels in parentheses were also calculated using Goodman-Kruskal’s gamma test (the null hypothesis being that the correlation is different from zero).</p>c<p>Using the R-package correlation test (<a href="http://personality-project.org/r/html/r.test.html" target="_blank">http://personality-project.org/r/html/r.test.html</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067159#pone.0067159-Steiger1" target="_blank">[62]</a>) the correlation between phenotypic potential and game centrality was significantly larger than the correlation between phenotypic potential and degree, or the correlation between phenotypic potential and betweenness centrality.</p