The storage of hydrogen in the form of metal hydrides: An application to thermal engines

The possibility of using LaNi56, FeTiH2, or MgH2 as metal hydride storage sytems for hydrogen fueled automobile engines is discussed. Magnesium copper and magnesium nickel hydrides studies indicate that they provide more stable storage systems than pure magnesium hydrides. Several test engines employing hydrogen fuel have been developed: a single cylinder motor originally designed for use with air gasoline mixture; a four-cylinder engine modified to run on an air hydrogen mixture; and a gas turbine

Polymyxin-Resistant Acinetobacter spp. Isolates: What is Next?

Univ Fed Sao Paulo, Div Infect Dis, Lab Especial Microbiol Clin, BR-04025010 Sao Paulo, SP, BrazilUniv Fed Sao Paulo, Div Infect Dis, Lab Especial Microbiol Clin, BR-04025010 Sao Paulo, SP, BrazilWeb of Scienc

Speaker adaptation and adaptive training for jointly optimised tandem systems

Speaker independent (SI) Tandem systems trained by joint optimisation of bottleneck (BN) deep neural networks (DNNs) and Gaussian mixture models (GMMs) have been found to produce similar word error rates (WERs) to Hybrid DNN systems. A key advantage of using GMMs is that existing speaker adaptation methods, such as maximum likelihood linear regression (MLLR), can be used which to account for diverse speaker variations and improve system robustness. This paper investigates speaker adaptation and adaptive training (SAT) schemes for jointly optimised Tandem systems. Adaptation techniques investigated include constrained MLLR (CMLLR) transforms based on BN features for SAT as well as MLLR and parameterised sigmoid functions for unsupervised test-time adaptation. Experiments using English multi-genre broadcast (MGB3) data show that CMLLR SAT yields a 4% relative WER reduction over jointly trained Tandem and Hybrid SI systems, and further reductions in WER are obtained by system combination

Improving Interpretability and Regularization in Deep Learning

IEEE Deep learning approaches yield state-of-the-art performance in a range of tasks, including automatic speech recognition. However, the highly distributed representation in a deep neural network (DNN) or other network variations are difficult to analyse, making further parameter interpretation and regularisation challenging. This paper presents a regularisation scheme acting on the activation function output to improve the network interpretability and regularisation. The proposed approach, referred to as activation regularisation, encourages activation function outputs to satisfy a target pattern. By defining appropriate target patterns, different learning concepts can be imposed on the network. This method can aid network interpretability and also has the potential to reduce over-fitting. The scheme is evaluated on several continuous speech recognition tasks: the Wall Street Journal continuous speech recognition task, eight conversational telephone speech tasks from the IARPA Babel program and a U.S. English broadcast news task. On all the tasks, the activation regularisation achieved consistent performance gains over the standard DNN baselines

Stimulated training for automatic speech recognition and keyword search in limited resource conditions

© 2017 IEEE. Training neural network acoustic models on limited quantities of data is a challenging task. A number of techniques have been proposed to improve generalisation. This paper investigates one such technique called stimulated training. It enables standard criteria such as cross-entropy to enforce spatial constraints on activations originating from different units. Having different regions being active depending on the input unit may help network to discriminate better and as a consequence yield lower error rates. This paper investigates stimulated training for automatic speech recognition of a number of languages representing different families, alphabets, phone sets and vocabulary sizes. In particular, it looks at ensembles of stimulated networks to ensure that improved generalisation will withstand system combination effects. In order to assess stimulated training beyond 1-best transcription accuracy, this paper looks at keyword search as a proxy for assessing quality of lattices. Experiments are conducted on IARPA Babel program languages including the surprise language of OpenKWS 2016 competition