Wyniki wyszukiwania - Biblioteka Nauki

1

Animal mimicry for covert communication with arbitrary output distribution: beyond the assumption of ignorance

100%

Zuber K. W. , Opieliński K. J.

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tom Vol. 30, nr 1

art. no. 2019119

EN

The paper describes a new method of embedding human communication in acoustic sequences mimicking animal communication. This is done to ensure a low probability of detection (LPD) transfer of covert messages. The proposed scheme mimics not only individual sounds, but also the imitated species’ communication structure. This paper presents a step forward in animal communication mimicry - from pure vocal imitation without regard for the plausibility of communication’s structure, through Zipf’s law-preserving scheme, to the mimicry of a known communication structure. Unlike previous methods, the updated scheme does not rely on third parties’ ignorance of the imitated species’ communication structure beyond Zipf’s law - instead, the new method enables one to encode information in a known zeroth-order Markov model. The paper describes a method of encoding an arbitrary message in a syntactically plausible, species-specific sequence of animal sounds through evolutionary means. A comparison with the previous iteration of the method is also presented.

2

Times series averaging and denoising from a probabilistic perspective on time-elastic kernels

100%

Marteau P.-F.

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tom Vol. 29, no. 2

375--392

EN

In the light of regularized dynamic time warping kernels, this paper re-considers the concept of a time elastic centroid for a set of time series. We derive a new algorithm based on a probabilistic interpretation of kernel alignment matrices. This algorithm expresses the averaging process in terms of stochastic alignment automata. It uses an iterative agglomerative heuristic method for averaging the aligned samples, while also averaging the times of their occurrence. By comparing classification accuracies for 45 heterogeneous time series data sets obtained by first nearest centroid/medoid classifiers, we show that (i) centroid-based approaches significantly outperform medoid-based ones, (ii) for the data sets considered, our algorithm, which combines averaging in the sample space and along the time axes, emerges as the most significantly robust model for time-elastic averaging with a promising noise reduction capability. We also demonstrate its benefit in an isolated gesture recognition experiment and its ability to significantly reduce the size of training instance sets. Finally, we highlight its denoising capability using demonstrative synthetic data. Specifically, we show that it is possible to retrieve, from few noisy instances, a signal whose components are scattered in a wide spectral band.

3

On Naive Bayes in Speech Recognition

100%

Toth L. , Kocsor A. , Csirik J.

International Journal of Applied Mathematics and Computer Science

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2005

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tom Vol. 15, no 2

287-294

EN

The currently dominant speech recognition technology, hidden Markov modeling, has long been criticized for its simplistic assumptions about speech, and especially for the naive Bayes combination rule inherent in it. Many sophisticated alternative models have been suggested over the last decade. These, however, have demonstrated only modest improvements and brought no paradigm shift in technology. The goal of this paper is to examine why HMM performs so well in spite of its incorrect bias due to the naive Bayes assumption. To do this we create an algorithmic framework that allows us to experiment with alternative combination schemes and helps us understand the factors that influence recognition performance. From the findings we argue that the bias peculiar to the naive Bayes rule is not really detrimental to phoneme classification performance. Furthermore, it ensures consistent behavior in outlier modeling, allowing efficient management of insertion and deletion errors.

4

Integration of hidden markov models in the automated speaker recognition system for critical use

100%

Kovtun V. V. , Yukhimchuk M. S. , Kisała P. , Abisheva A. , Rakhmetullina S.

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2019

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tom R. 95, nr 4

176--180

EN

In this article, the author theoretically substantiated the possibility of integration of hidden Markov models (IHMM) in the structure of the automated speaker recognition system for critical use (ASRSCU) for analysis of speech information from a plurality of independent input channels, which allowed within the statistical conception of pattern recognition to combine the accuracy of the approximation of input signals inherent the apparatus of GMM models. The authors proposed a mathematical apparatus for the integration of hidden Markov models, which allows us to adequately describe the set of interacting processes in the Markov paradigm with the preservation of temporal, asymmetric conditional probabilities between the chains.

PL

W tym artykule autorzy teoretycznie uzasadnili możliwość integracji ukrytych modeli Markowa (IHMM) w strukturze zautomatyzowanego systemu rozpoznawania głosu osoby mówiącej do zastosowań krytycznych (ASRSCU) do analizy informacji o mowie z wielu niezależnych kanałów wejściowych, które dopuszczają wewnątrz statystyczna koncepcję rozpoznawania wzorców w celu połączenia dokładności aproksymacji sygnałów wejściowych z aparatem modeli GMM. Autorzy zaproponowali aparat matematyczny do integracji ukrytych modeli Markowa, który pozwala odpowiednio opisać zestaw oddziałujących procesów w paradygmacie Markowa z zachowaniem czasowych, asymetrycznych warunkowych prawdopodobieństw między łańcuchami.

5

Hidden Markov Models as a Tool for the Assessment of Dependence of Phenomena of Economic Nature

94%

Bernardelli M.

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tom 5

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nr 338

7-20

PL

Ocena zależności między szeregami czasowymi jest zagadnieniem, które jest często rozwiązywane za pomocą współczynnika korelacji Pearsona. Niestety, czasami wyniki mogą być bardzo mylące. W artykule przedstawiono alternatywną miarę badania zależności, opartą na ukrytych modelach Markowa oraz ścieżkach Viterbiego. Zaproponowana metoda nie jest uniwersalna, ale wydaje się dość dokładnie odzwierciedlać podobieństwo między szeregami czasowymi, eksponując okresy zbieżności i rozbieżności. Przydatność tej nowej miary została zweryfikowana na przykładach, jak również realnych danych makroekonomicznych. Zaletami tej metody są: słabe założenia stosowalności, łatwość interpretacji wyników, możliwość generalizacji i wysoka skuteczność w ocenie zależności różnych szeregów czasowych o charakterze ekonomicznym. Nie należy jej jednak traktować jako substytutu korelacji Pearsona, a raczej jako uzupełniającą metodę pomiaru zależności.

EN

The assessment of dependence between time series is a common dilemma, which is often solved by the use of the Pearson’s correlation coefficient. Unfortunately, sometimes, the results may be highly misleading. In this paper, an alternative measure is presented. It is based on hidden Markov models and Viterbi paths. The proposed method is in no way universal but seems to provide quite an accurate image of the similarities between time series, by disclosing the periods of convergence and divergence. The usefulness of this new measure is verified by specially crafted examples and real‑life macroeconomic data. There are some definite advantages to this method: the weak assumptions of applicability, ease of interpretation of the results, possibility of easy generalization, and high effectiveness in assessing the dependence of different time series of an economic nature. It should not be treated as a substitute for the Pearson’s correlation, but rather as a complementary method of dependence measure.

6

Chemotherapy-induced fatigue estimation using hidden Markov model

84%

Ameli S. , Naghdy F. , Stirling D. , Naghdy G. , Aghmesheh M.

Biocybernetics and Biomedical Engineering

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2019

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tom Vol. 39, no. 1

176--187

EN

Chemotherapy-induced fatigue undermines the physical performance and alter gait behaviour of patients. In clinics, there is not a well-established method to objectively assess the effects of chemotherapy-induced fatigue on gait characteristics. Clinical trials commonly use 6 Minute Walking Tests (6MWT) to assess patients' gait. However, these studies only measure the distance that patients can walk. The distance does not provide comprehensive information about variations in ambulatory motion characteristics and body postural behaviour which can more appropriately describe the fatigue effects on general physical performance. Gait characteristics provide a manifestation of relationships between muscular and cardiovascular fitness status and physical motions. Hence, an assessment of gait characteristics provides more appropriate information about the effects of chemotherapy-induced fatigue on gait behaviour. A novel approach is proposed to objectively assess the impacts of chemotherapy-induced fatigue on cancer gait by analysing the gait characteristics during 6MWT. The joint angles of the lower body segments are measured by inertial sensors and modelled through a Hidden Markov Model (HMM) with Gaussian emissions. A Gaussian clustering method classifies the joint angles of first gait cycle to determine the six gait phases of a normal gait as initial training values. A comparison of gait characteristics before and after chemotherapy-induced fatigue determines the gait abnormalities. The method is applied to four cancer patients and outcomes are benchmarked against the gait of a healthy subject before and after running program-induced fatigue. The results indicate a more accurate quantitative-based tool to measure the effects of chemotherapy-induce fatigue on gait and physical performance.

7

Multimodal face recognition method with two-dimensional hidden Markov model

84%

Bobulski J.

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tom Vol. 65, nr 1

121—128

EN

The paper presents a new solution for the face recognition based on two-dimensional hidden Markov models. The traditional HMM uses one-dimensional data vectors, which is a drawback in the case of 2D and 3D image processing, because part of the information is lost during the conversion to one-dimensional features vector. The paper presents a concept of the full ergodic 2DHMM, which can be used in 2D and 3D face recognition. The experimental results demonstrate that the system based on two dimensional hidden Markov models is able to achieve a good recognition rate for 2D, 3D and multimodal (2D+3D) face images recognition, and is faster than ICP method.

8

A Longitudinal Study of Polish Attitudes to Emigration: A Latent Markov Model Approach

84%

Genge E.

Argumenta Oeconomica Cracoviensia

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2017

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nr 16

37-49

EN

Latent class analysis can be viewed as a special case of model-based clustering for multivariate discrete data. When longitudinal data are to be analysed, the research questions concern some form of change over time. The latent Markov model is a variation of the latent class model that is applied to estimate not only the prevalence of latent class membership, but the incidence of transitions over time in latent class membership. In 2004, Poland joined the European Union, prompting a number of Poles to leave the country. To examine this event, a model-based clustering approach for grouping and detecting inhomogeneities of public attitudes to emigration from Poland was used. It focuses especially on latent Markov models with covariates, which additionally made it possible to investigate the dynamic pattern of Poles’ attitudes to emigration for different demographic features. depmixS4, Rsolnp and LMest packages of R were used.

PL

Modele mieszanek, których składowe charakteryzowane są przez rozkłady prawdopodobieństw, reprezentują tzw. podejście modelowe w taksonomii. Obecnie coraz popularniejsze są modele mieszanek w analizie danych panelowych, w której celem jest już nie tylko podział obserwacji na homogeniczne grupy, ale również pewna analiza zmian w czasie. W takim przypadku stosowane są ukryte modele Markowa. W 2014 r. minęło 10 lat od przystąpienia Polski do Unii Europejskiej. Okres taki pozwala na dokonanie analizy nastawienia Polaków do emigracji. Celem badań jest podział Polaków na klasy o podobnym nastawieniu do emigracji w latach 2004–2013. Analiza empiryczna przeprowadzona została za pomocą ukrytych modeli Markowa z uwzględnieniem zmiennych towarzyszących. Wykorzystane zostały pakiety depmixS4, Rsolnp oraz LMest programu R.

9

Automatic detection of prolonged fricative phonemes with the Hidden Markov Models approach

84%

Wiśniewski M. , Kuniszyk-Jóźkowiak W. , Smołka E. , Suszyński W.

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2007

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tom Vol. 11

293--297

EN

The Hidden Markov Model (HMM) is a stochastic approach to recognition of patterns appearing in an input signal. In the work author's implementation of the HMM were used to recognize speech disorders - prolonged fricative phonemes. To achieve the best recognition effectiveness and simultaneously preserve reasonable time required for calculations two problems need to be addressed: the choice of the HMM and the proper preparation of an input data. Tests results for recognition of the considered type of speech disorders are presented for HMM models with different number of states and for different sizes of codebooks.

10

O Ukrytych Modelach Markowa i ich zastosowaniu do rozpoznawania ciągów znaków pisma odręcznego

67%

Klęsk P.

Metody Informatyki Stosowanej

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2008

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tom nr 1 (Tom 13)

175-189

EN

In the paper, selected informations on Hidden Markov Models (also called Hidden Markov Chains) are reminded. Basic riotions are defined and algorithms related to these models are shortly presented. The research part of the papers shows results of three conducted experiments entitled: "pork cutlet", "form sheet" and ''poetaster". The most important experiment "form sheet" gives a good starting point to a practical application of HMMs to the. handwriting recognition. The "poetaster" experiment shows possible application of HMMs in so called "artifial creation".

11

HMM-based phoneme speech recognition system for the control and command of industrial robots

67%

Naik A.

Technical Transactions

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2021

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tom Vol. 118, iss. 1

art. no. e2021002

EN

n recent years, the integration of human-robot interaction with speech recognition has gained a lot of pace in the manufacturing industries. Conventional methods to control the robots include semi-autonomous, fully-autonomous, and wired methods. Operating through a teaching pendant or a joystick is easy to implement but is not effective when the robot is deployed to perform complex repetitive tasks. Speech and touch are natural ways of communicating for humans and speech recognition, being the best option, is a heavily researched technology. In this study, we aim at developing a stable and robust speech recognition system to allow humans to communicate with machines (robotic-arm) in a seamless manner. This paper investigates the potential of the linear predictive coding technique to develop a stable and robust HMM-based phoneme speech recognition system for applications in robotics. Our system is divided into three segments: a microphone array, a voice module, and a robotic arm with three degrees of freedom (DOF). To validate our approach, we performed experiments with simple and complex sentences for various robotic activities such as manipulating a cube and pick and place tasks. Moreover, we also analyzed the test results to rectify problems including accuracy and recognition score.

12

Plantar pressure image fusion for comfort fusion in diabetes mellitus using an improved fuzzy hidden Markov model

67%

Li Z. , Wang D. , Dey N. , Ashour A. S. , Sherratt R. S. , Shi F.

Biocybernetics and Biomedical Engineering

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2019

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tom Vol. 39, no. 3

742--752

EN

Diabetes mellitus is a clinical syndrome caused by the interaction of genetic and environmental factors. The change of plantar pressure in diabetic patients is one of the important reasons for the occurrence of diabetic foot. The abnormal increase of plantar pressure is a predictor of the common occurrence of foot ulcers. The feature extraction of plantar pressure distribution will be beneficial to the design and manufacture of diabetic shoes that will be beneficial for early protection of diabetes mellitus patients. In this research, texture-based features of the angular second moment (ASM), moment of inertia (MI), inverse difference monument (IDM), and entropy (E) have been selected and fused by using the updown algorithm. The fused features are normalized to predict comfort plantar pressure imaging dataset using an improved fuzzy hidden Markov model (FHMM). In FHMM, type-I fuzzy set is proposed and fuzzy Baum–Welch algorithm is also applied to estimate the next features. The results are discussed, and by comparing with other back–forward algorithms and different fusion operations in FHMM. Improved HMMs with up–down fusion using type-I fuzzy definition performs high effectiveness in prediction comfort plantar pressure distribution in an image dataset with an accuracy of 82.2% and the research will be applied to the shoe-last personalized customization in the industry.