Language Identification in Short Utterances Using Long Short-Term Memory (LSTM) Recurrent Neural Networks

Zazo R, Lozano-Diez A, Gonzalez-Dominguez J, T. Toledano D, Gonzalez-Rodriguez J (2016) Language Identification in Short Utterances Using Long Short-Term Memory (LSTM) Recurrent Neural Networks. PLoS ONE 11(1): e0146917. doi:10.1371/journal.pone.0146917Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and Deep Neural Networks (DNNs), in automatic Language Identification (LID), particularly when dealing with very short utterances (similar to 3s). In this contribution we present an open-source, end-to-end, LSTM RNN system running on limited computational resources (a single GPU) that outperforms a reference i-vector system on a subset of the NIST Language Recognition Evaluation (8 target languages, 3s task) by up to a 26%. This result is in line with previously published research using proprietary LSTM implementations and huge computational resources, which made these former results hardly reproducible. Further, we extend those previous experiments modeling unseen languages (out of set, OOS, modeling), which is crucial in real applications. Results show that a LSTM RNN with OOS modeling is able to detect these languages and generalizes robustly to unseen OOS languages. Finally, we also analyze the effect of even more limited test data (from 2.25s to 0.1s) proving that with as little as 0.5s an accuracy of over 50% can be achieved.This work has been supported by project CMC-V2: Caracterizacion, Modelado y Compensacion de Variabilidad en la Señal de Voz (TEC2012-37585-C02-01), funded by Ministerio de Economia y Competitividad, Spain

Restoring speech following total removal of the larynx by a learned transformation from sensor data to acoustics

Total removal of the larynx may be required to treat laryngeal cancer: speech is lost. This article shows that it may be possible to restore speech by sensing movement of the remaining speech articulators and use machine learning algorithms to derive a transformation to convert this sensor data into an acoustic signal. The resulting “silent speech,” which may be delivered in real time, is intelligible and sounds natural. The identity of the speaker is recognisable. The sensing technique involves attaching small, unobtrusive magnets to the lips and tongue and monitoring changes in the magnetic field induced by their movement

Star-forming cores embedded in a massive cold clump: Fragmentation, collapse and energetic outflows

The fate of massive cold clumps, their internal structure and collapse need to be characterised to understand the initial conditions for the formation of high-mass stars, stellar systems, and the origin of associations and clusters. We explore the onset of star formation in the 75 M_sun SMM1 clump in the region ISOSS J18364-0221 using infrared and (sub-)millimetre observations including interferometry. This contracting clump has fragmented into two compact cores SMM1 North and South of 0.05 pc radius, having masses of 15 and 10 M_sun, and luminosities of 20 and 180 L_sun. SMM1 South harbours a source traced at 24 and 70um, drives an energetic molecular outflow, and appears supersonically turbulent at the core centre. SMM1 North has no infrared counterparts and shows lower levels of turbulence, but also drives an outflow. Both outflows appear collimated and parsec-scale near-infrared features probably trace the outflow-powering jets. We derived mass outflow rates of at least 4E-5 M_sun/yr and outflow timescales of less than 1E4 yr. Our HCN(1-0) modelling for SMM1 South yielded an infall velocity of 0.14 km/s and an estimated mass infall rate of 3E-5 M_sun/yr. Both cores may harbour seeds of intermediate- or high-mass stars. We compare the derived core properties with recent simulations of massive core collapse. They are consistent with the very early stages dominated by accretion luminosity.Comment: Accepted for publication in ApJ, 14 pages, 7 figure

Ecological changes in historically polluted soils: Metal(loid) bioaccumulation in microarthropods and their impact on community structure

International audienceSoil pollution by persistent metal(loid)s present environmental and sanitary risks. While the effects of metal(loid)s on vegetation and macrofauna have been widely studied, their impact on microarthropods (millimetre scale) and their bioaccumulation capacity have been less investigated. However, microarthropods provide important ecosystem services, contributing in particular to soil organic matter dynamics. This study focussed on the impact of metal(loid) pollution on the structure and distribution of microarthropod communities and their potential to bioaccumulate lead (Pb). Soil samples were collected from a contaminated historical site with a strong horizontal and vertical gradient of Pb concentrations. Microarthropods were extracted using the Berlese method. The field experiments showed that microarthropods were present even in extremely polluted soils (30,000 mg Pb kg− 1). However, while microarthropod abundance increased with increasing soil C/N content (R2 = 0.79), richness decreased with increasing pollution. A shift in the community structure from an oribatid-to a springtail-dominated community was observed in less polluted soils (R2 = 0.68). In addition, Pb bioamplification occurred in microarthropods, with higher Pb concentrations in predators than in detritivorous microarthropods. Finally, the importance of feeding and reproductive ecological traits as potentially relevant descriptors of springtail community structures was highlighted. This study demonstrates the interest of microarthropod communities with different trophic levels and ecological features for evaluating the global environmental impact of metal(loid) pollution on soil biological quality

Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network

Abstract Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. Methods Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. Results Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. Conclusion By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals

HIV-1 Vpu Neutralizes the Antiviral Factor Tetherin/BST-2 by Binding It and Directing Its Beta-TrCP2-Dependent Degradation

Host cells impose a broad range of obstacles to the replication of retroviruses. Tetherin (also known as CD317, BST-2 or HM1.24) impedes viral release by retaining newly budded HIV-1 virions on the surface of cells. HIV-1 Vpu efficiently counteracts this restriction. Here, we show that HIV-1 Vpu induces the depletion of tetherin from cells. We demonstrate that this phenomenon correlates with the ability of Vpu to counteract the antiviral activity of both overexpressed and interferon-induced endogenous tetherin. In addition, we show that Vpu co-immunoprecipitates with tetherin and β-TrCP in a tri-molecular complex. This interaction leads to Vpu-mediated proteasomal degradation of tetherin in a β-TrCP2-dependent manner. Accordingly, in conditions where Vpu-β-TrCP2-tetherin interplay was not operative, including cells stably knocked down for β-TrCP2 expression or cells expressing a dominant negative form of β-TrCP, the ability of Vpu to antagonize the antiviral activity of tetherin was severely impaired. Nevertheless, tetherin degradation did not account for the totality of Vpu-mediated counteraction against the antiviral factor, as binding of Vpu to tetherin was sufficient for a partial relief of the restriction. Finally, we show that the mechanism used by Vpu to induce tetherin depletion implicates the cellular ER-associated degradation (ERAD) pathway, which mediates the dislocation of ER membrane proteins into the cytosol for subsequent proteasomal degradation. In conclusion, we show that Vpu interacts with tetherin to direct its β-TrCP2-dependent proteasomal degradation, thereby alleviating the blockade to the release of infectious virions. Identification of tetherin binding to Vpu provides a potential novel target for the development of drugs aimed at inhibiting HIV-1 replication

Humanized Mice Recapitulate Key Features of HIV-1 Infection: A Novel Concept Using Long-Acting Anti-Retroviral Drugs for Treating HIV-1

BACKGROUND: Humanized mice generate a lymphoid system of human origin subsequent to transplantation of human CD34+ cells and thus are highly susceptible to HIV infection. Here we examined the efficacy of antiretroviral treatment (ART) when added to food pellets, and of long-acting (LA) antiretroviral compounds, either as monotherapy or in combination. These studies shall be inspiring for establishing a gold standard of ART, which is easy to administer and well supported by the mice, and for subsequent studies such as latency. Furthermore, they should disclose whether viral breakthrough and emergence of resistance occurs similar as in HIV-infected patients when ART is insufficient. METHODS/PRINCIPAL FINDINGS: NOD/shi-scid/γ(c)null (NOG) mice were used in all experimentations. We first performed pharmacokinetic studies of the drugs used, either added to food pellets (AZT, TDF, 3TC, RTV) or in a LA formulation that permitted once weekly subcutaneous administration (TMC278: non-nucleoside reverse transcriptase inhibitor, TMC181: protease inhibitor). A combination of 3TC, TDF and TMC278-LA or 3TC, TDF, TMC278-LA and TMC181-LA suppressed the viral load to undetectable levels in 15/19 (79%) and 14/14 (100%) mice, respectively. In successfully treated mice, subsequent monotherapy with TMC278-LA resulted in viral breakthrough; in contrast, the two LA compounds together prevented viral breakthrough. Resistance mutations matched the mutations most commonly observed in HIV patients failing therapy. Importantly, viral rebound after interruption of ART, presence of HIV DNA in successfully treated mice and in vitro reactivation of early HIV transcripts point to an existing latent HIV reservoir. CONCLUSIONS/SIGNIFICANCE: This report is a unique description of multiple aspects of HIV infection in humanized mice that comprised efficacy testing of various treatment regimens, including LA compounds, resistance mutation analysis as well as viral rebound after treatment interruption. Humanized mice will be highly valuable for exploring the antiviral potency of new compounds or compounds targeting the latent HIV reservoir

Learning Context Sensitive Languages with LSTM Trained with Kalman Filters

Unlike traditional recurrent neural networks, the long shortterm memory (LSTM) model generalizes well when presented with training sequences derived from regular and also simple nonregular languages