Sequential design of computer experiments for the estimation of a probability of failure

This paper deals with the problem of estimating the volume of the excursion set of a function $f:\mathbb{R}^d \to \mathbb{R}$ above a given threshold, under a probability measure on $\mathbb{R}^d$ that is assumed to be known. In the industrial world, this corresponds to the problem of estimating a probability of failure of a system. When only an expensive-to-simulate model of the system is available, the budget for simulations is usually severely limited and therefore classical Monte Carlo methods ought to be avoided. One of the main contributions of this article is to derive SUR (stepwise uncertainty reduction) strategies from a Bayesian-theoretic formulation of the problem of estimating a probability of failure. These sequential strategies use a Gaussian process model of $f$ and aim at performing evaluations of $f$ as efficiently as possible to infer the value of the probability of failure. We compare these strategies to other strategies also based on a Gaussian process model for estimating a probability of failure.Comment: This is an author-generated postprint version. The published version is available at http://www.springerlink.co

Multilingual representations for low resource speech recognition and keyword search

© 2015 IEEE. This paper examines the impact of multilingual (ML) acoustic representations on Automatic Speech Recognition (ASR) and keyword search (KWS) for low resource languages in the context of the OpenKWS15 evaluation of the IARPA Babel program. The task is to develop Swahili ASR and KWS systems within two weeks using as little as 3 hours of transcribed data. Multilingual acoustic representations proved to be crucial for building these systems under strict time constraints. The paper discusses several key insights on how these representations are derived and used. First, we present a data sampling strategy that can speed up the training of multilingual representations without appreciable loss in ASR performance. Second, we show that fusion of diverse multilingual representations developed at different LORELEI sites yields substantial ASR and KWS gains. Speaker adaptation and data augmentation of these representations improves both ASR and KWS performance (up to 8.7% relative). Third, incorporating un-transcribed data through semi-supervised learning, improves WER and KWS performance. Finally, we show that these multilingual representations significantly improve ASR and KWS performance (relative 9% for WER and 5% for MTWV) even when forty hours of transcribed audio in the target language is available. Multilingual representations significantly contributed to the LORELEI KWS systems winning the OpenKWS15 evaluation

Variability in the articulation and perception of a word

The words making up a speaker’s mental lexicon may be stored as abstract phonological representations or else they may be stored as detailed acoustic-phonetic representations. The speaker’s articulatory gestures intended to represent a word show relatively high variability in spontaneous speech. The aim of this paper is to explore the acoustic-phonetic patterns of the Hungarian word akkor ‘then, at that time’. Ten speakers’ recorded spontaneous speech with a total duration of 255 minutes and containing 286 occurrences of akkor were submitted to analysis. Durational and frequency patterns were measured by means of the Praat software. The results obtained show higher variability both within and across speakers than it had been expected. Both the durations of the words and those of the speech sounds, as well as the vowel formants, turned out to significantly differ across speakers. In addition, the results showed considerable within-speaker variation as well. The correspondence between variability in the objective acoustic-phonetic data and the flexibility and adaptive nature of the mental representation of a word will be discussed.For the perception experiments, two speakers of the previous experiment were selected whose 48 words were then used as speech material. The listeners had to judge the quality of the words they heard using a five-point scale. The results confirmed that the listeners used diverse strategies and representations depending on the acoustic-phonetic parameters of the series of occurrences of akkor

Communications Biophysics

Contains reports on ten research projects.National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Training Grant 5 T32 NS0704)National Science Foundation (Grant BNS80-06369)National Institutes of Health (Grant 5 R01 NS11153)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 RO1 NS12846)National Science Foundation (Grant BNS77-21751)National Institutes of Health (Grant 1 P01 NS14092)Karmazin Foundation through the Council for the Arts at MITNational Institutes of Health (Fellowship 5 F32 NS06386)National Science Foundation (Fellowship SP179-14913)National Institutes of Health (Grant 5 RO1 NS11080

Communications Biophysics

Contains reports on four research projects.National Institutes of Health (Grant 5 P01 NS13126-02)National Institutes of Health (Grant 5 K04 NS00113-03)National Institutes of Health (Grant 2 ROI NS11153-02A1)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 RO1 NS10916-03)National Institutes of Health (Fellowship 1 F32 NS05327)National Institutes of Health (Grant 5 ROI NS12846-02)National Institutes of Health (Fellowship 1 F32 NS05266)Edith E. Sturgis FoundationNational Institutes of Health (Grant 1 R01 NS11680-01)National Institutes of Health (Grant 2 RO1 NS11080-04)National Institutes of Health (Grant 5 T32 GIM107301-03)National Institutes of Health (Grant 5 TOI GM01555-10

Communications Biophysics

Contains research objectives and summary of research on nine research projects split into four sections.National Institutes of Health (Grant 5 ROI NS11000-03)National Institutes of Health (Grant 1 P01 NS13126-01)National Institutes of Health (Grant 1 RO1 NS11153-01)National Institutes of Health (Grant 2 R01 NS10916-02)Harvard-M.I.T. Rehabilitation Engineering CenterU. S. Department of Health, Education, and Welfare (Grant 23-P-55854)National Institutes of Health (Grant 1 ROl NS11680-01)National Institutes of Health (Grant 5 ROI NS11080-03)M.I.T. Health Sciences Fund (Grant 76-07)National Institutes of Health (Grant 5 T32 GM07301-02)National Institutes of Health (Grant 5 TO1 GM01555-10

Communications Biophysics

Contains reports on nine research projects split into four sections.National Institutes of Health (Grant 5 PO1 NS13126)National Institutes of Health (Grant 5 KO4 NS00113)National Institutes of Health (Training Grant 5 T32 NS07047)National Institutes of Health (Training Grant 1 T32 NS07099)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 ROI NS10916)National Institutes of Health (Grant 5 RO1 NS12846)National Science Foundation (Grant BNS77-21751)National Institutes of Health (Grant 1 RO1 NS14092)Edith E. Sturgis FoundationHealth Sciences FundNational Institutes of Health (Grant 2 R01 NS11680)National Institutes of Health (Fellowship 5 F32 NS05327)National Institutes of Health (Grant 2 ROI NS11080)National Institutes of Health (Training Grant 5 T32 GM07301

Communications Biophysics

Contains reports on eight research projects split into four sections.National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 5 K04 NS00113)National Institutes of Health (Training Grant 5 T32 NS07047)National Science Foundation (Grant BNS80-06369)National Institutes of Health (Grant 5 ROl NS11153)National Institutes of Health (Fellowship 1 F32 NS06544)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 R01 NS10916)National Institutes of Health (Grant 5 RO1 NS12846)National Science Foundation (Grant BNS77-21751)National Institutes of Health (Grant 1 R01 NS14092)National Institutes of Health (Grant 2 R01 NS11680)National Institutes of Health (Grant 5 ROl1 NS11080)National Institutes of Health (Training Grant 5 T32 GM07301

Communications Biophysics

Contains reports on nine research projects split into four sections.National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 5 K04 NS00113)National Institutes of Health (Training Grant 5 T32 NS07047)National Institutes of Health (Grant 5 ROl NS11153-03)National Institutes of Health (Fellowship 1 T32 NS07099-01)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 ROl NS10916)National Institutes of Health (Grant 5 ROl NS12846)National Science Foundation (Grant BNS77-21751)National Institutes of Health (Grant 1 RO1 NS14092)Health Sciences FundNational Institutes of Health (Grant 2 R01 NS11680)National Institutes of Health (Grant 2 RO1 NS11080)National Institutes of Health (Training Grant 5 T32 GM07301