Benefit of Mumble Model to the Czech Telephone Dialogue System

This paper discusses a usage of a mumble model in a Czech telephone dialogue system designed and constructed at the Department of Cybernetics, University of West Bohemia, and describes benefits of the mumble model to speech recognition, namely to a rejection method. Firstly, the overview of the Czech telephone dialogue system and its recognition engine is given. The recognition is based on a statistical approach. The triphones are used and modeled by tree−state left−to−right HMMs with an output probability density function expressed as a multivariate Gaussian mixture. The stochastic regular grammars are used as a language model to reduce a task perplexity. Secondly, the mumble model is introduced as a recursive network of Czech phones HMM models connected in parallel, and an implementation of a rejection and a key−word spotting method, both based on the mumble model, is explained. Finally, the experimental results providing the 19.4 % equal error rate (EER) of the rejection and 16.7 % EER of the key−word spotting method are discussed. 1

Bayesian inverse modeling and source location of an unintended ¹³¹I release in Europe in the fall of 2011

In the fall of 2011, iodine-131 (131I) was detected at several radionuclide monitoring stations in central Europe. After investigation, the International Atomic Energy Agency (IAEA) was informed by Hungarian authorities that 131I was released from the Institute of Isotopes Ltd. in Budapest, Hungary. It was reported that a total activity of 342 GBq of 131I was emitted between 8 September and 16 November 2011. In this study, we use the ambient concentration measurements of 131I to determine the location of the release as well as its magnitude and temporal variation. As the location of the release and an estimate of the source strength became eventually known, this accident represents a realistic test case for inversion models. For our source reconstruction, we use no prior knowledge. Instead, we estimate the source location and emission variation using only the available 131I measurements. Subsequently, we use the partial information about the source term available from the Hungarian authorities for validation of our results. For the source determination, we first perform backward runs of atmospheric transport models and obtain source-receptor sensitivity (SRS) matrices for each grid cell of our study domain. We use two dispersion models, FLEXPART and Hysplit, driven with meteorological analysis data from the global forecast system (GFS) and from European Centre for Medium-range Weather Forecasts (ECMWF) weather forecast models. Second, we use a recently developed inverse method, least-squares with adaptive prior covariance (LS-APC), to determine the 131I emissions and their temporal variation from the measurements and computed SRS matrices. For each grid cell of our simulation domain, we evaluate the probability that the release was generated in that cell using Bayesian model selection. The model selection procedure also provides information about the most suitable dispersion model for the source term reconstruction. Third, we select the most probable location of the release with its associated source term and perform a forward model simulation to study the consequences of the iodine release. Results of these procedures are compared with the known release location and reported information about its time variation. We find that our algorithm could successfully locate the actual release site. The estimated release period is also in agreement with the values reported by IAEA and the reported total released activity of 342 GBq is within the 99 % confidence interval of the posterior distribution of our most likely model

Rychlé fonetické/lexikální hledání v archivech výpovědí českého holokaustu: směřování k cílům projektu MALACH

Tento článek popisuje systém pro rychlé fonetické/lexikální prohledávání velkého audiovizuálního archívu českého holokaustu. Popisovaný systém je prvním krokem k naplnění projektu Malach. Více než 1000 hodin výpovědí bylo automaticky rozpoznáno a foneticky indexováno. Speciální pozornost byla věnována nespisvným slovům.In this paper we describe the system for a fast phonetic/lexical searching in the large archives of the Czech holocaust testimonies. The developed system is the first step to a fulfillment of the MALACH project visions [1,2], at least as for an easier and faster access to the Czech part of the archives. More than one thousand hours of spontaneous, accented and highly emotional speech of Czech holocaust survivors stored at the USC Shoah Foundation Institute as videointerviews were automatically transcribed and phonetically/lexically indexed. Special attention was paid to processing of colloquial words that appear very frequently in the Czech spontaneous speech. The final access to the archives is very fast allowing to detect segments of interviews containing pronounced words, clusters of words presented in pre-defined time intervals, and also words that were not included in the working vocabulary (OOV words)

Systém pro rychlé lexikální a fonetické vyhledávání mluvených frází v archívu českého kulturního dědictví

The main objective of the work presented in this paper was to develop a complete system that would accomplish the original visions of the MALACH project. Those goals were to employ automatic speech recognition and information retrieval techniques to provide improved access to the large video archive containing recorded testimonies of the Holocaust survivors. The system has been so far developed for the Czech part of the archive only. It takes advantage of the state-of-the art speech recognition system tailored to the challenging properties of the recordings in the archive (elderly speakers, spontaneous speech, emotionally loaded content) and its close coupling with the actual search engine. The design of the algorithm adopting the spoken term detection approach is focused on the speed of the retrieval. The resulting system is able to search through the 1,000 hours of video constituting the Czech portion of the archive and find query word occurrences in the matter of seconds. The phonetic search implemented alongside the search based on the lexicon words allows to find even the words outside the ASR system lexicon such as names, geographic locations or Jewish slang

Detection and mapping of a toxic cloud using UAVs and emergent techniques

Unmanned aerial vehicles have gained a lot of interest in recent times, due to their potential use in several civil applications. This paper focuses on the use of an autonomous swarm of drones to detect and map a toxic cloud. A possible real-world scenario is the accidental release of hazardous gases into the air, resulting from fire or an explosion at an industrial site. The proposed method is based on the concept of swarm intelligence: each drone (agent) performs basic interactions with the environment and with other drones, without need for a centralized coordination technique. More precisely, the method combines collision avoidance, flocking, stigmergy-based communication, and a cloud exploration behavior called inside-outside. For the experiments we developed a simulator using the NetLogo environment, and tested different combinations of these emergent behaviors on two scenarios. Parameters were tuned using differential evolution and separate scenarios. Results show that the combined use of different emergent techniques is beneficial, as the proposed method outperformed random flight as well as an exhaustive search throughout the explored area. In addition, results show little variance considering two different cloud shapes

Academy of Sciences of the Czech Republic

Ústav teorie informace a automatizac

Online variational inference for state-space models with point-process observations

We present a variational Bayesian (VB) approach for the state and parameter inference of a state-space model with point-process observations, a physiologically plausible model for signal processing of spike data. We also give the derivation of a variational smoother, as well as an efficient online filtering algorithm, which can also be used to track changes in physiological parameters. Themethods are assessed on simulated data, and results are compared to expectation-maximization, as well as Monte Carlo estimation techniques, in order to evaluate the accuracy of the proposed approach. The VB filter is further assessed on a data set of taste-response neural cells, showing that the proposed approach can effectively capture dynamical changes in neural responses in real time