16,596 research outputs found

    Improved Noisy Student Training for Automatic Speech Recognition

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    Recently, a semi-supervised learning method known as "noisy student training" has been shown to improve image classification performance of deep networks significantly. Noisy student training is an iterative self-training method that leverages augmentation to improve network performance. In this work, we adapt and improve noisy student training for automatic speech recognition, employing (adaptive) SpecAugment as the augmentation method. We find effective methods to filter, balance and augment the data generated in between self-training iterations. By doing so, we are able to obtain word error rates (WERs) 4.2%/8.6% on the clean/noisy LibriSpeech test sets by only using the clean 100h subset of LibriSpeech as the supervised set and the rest (860h) as the unlabeled set. Furthermore, we are able to achieve WERs 1.7%/3.4% on the clean/noisy LibriSpeech test sets by using the unlab-60k subset of LibriLight as the unlabeled set for LibriSpeech 960h. We are thus able to improve upon the previous state-of-the-art clean/noisy test WERs achieved on LibriSpeech 100h (4.74%/12.20%) and LibriSpeech (1.9%/4.1%).Comment: 5 pages, 5 figures, 4 tables; v2: minor revisions, reference adde

    Long-term vascular access ports as a means of sedative administration in a rodent fMRI survival model

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    The purpose of this study is to develop a rodent functional magnetic resonance imaging (fMRI) survival model with the use of heparin-coated vascular access devices. Such a model would ease the administration of sedative agents, reduce the number of animals required in survival experiments and eliminate animal-to-animal variability seen in previous designs. Seven male Sprague-Dawley rats underwent surgical placement of an MRI-compatible vascular access port, followed by implantable electrode placement on the right median nerve. Functional MRI during nerve stimulation and resting-state functional connectivity MRI (fcMRI) were performed at times 0, 2, 4, 8 and 12 weeks postoperatively using a 9.4 T scanner. Anesthesia was maintained using intravenous dexmedetomidine and reversed using atipamezole. There were no fatalities or infectious complications during this study. All vascular access ports remained patent. Blood oxygen level dependent (BOLD) activation by electrical stimulation of the median nerve using implanted electrodes was seen within the forelimb sensory region (S1FL) for all animals at all time points. The number of activated voxels decreased at time points 4 and 8 weeks, returning to a normal level at 12 weeks, which is attributed to scar tissue formation and resolution around the embedded electrode. The applications of this experiment extend far beyond the scope of peripheral nerve experimentation. These vascular access ports can be applied to any survival MRI study requiring repeated medication administration, intravenous contrast, or blood sampling

    ON THE POTENTIAL FOR ILL-LOGIC WITH LOGICALLY DEFINED OUTCOMES

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    Logically defined outcomes are commonly used in medical diagnoses and epidemiological research. When missing values in the original outcomes exist, the method of handling the missingness can have unintended consequences, even if the original outcomes are missing completely at random. Complicating the issue is that the default behavior of standard statistical packages yields different results. In this paper, we consider two binary original outcomes, which are missing completely at random. For estimating the prevalence of a logically defined or outcome, we discuss the properties of four estimators: complete case estimator, all-available case estimator, maximum likelihood estimator (MLE), and moment-based estimator. With the exception of the all-available case estimator, the estimators are consistent. A simulation study is conducted to evaluate the finite sample performance of the four estimators and an analysis of hypertension data from the Sleep Heart Health Study is presented
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