Hidden Markov Models

Abstract. One hundred years removed from AA Markov's development of his chains, we take stock of the field he generated and the mathematical impression he left. As a tribute to Markov, we present what we consider to be the five greatest applications of Markov chains ...

An offline cursive handwriting recognition system, based on hybrid of Neural Networks (NN) and Hidden Markov Models (HMM), is described in this paper. Applying SegRec principle, the recognizer does not make hard decision at the character segmentation process. Instead, it delays the character segmentation to the recognition stage by generating a segmentation graph that describes all possible ways to segment a word into letters. To recognize a word, the NN computes the observation probabilities for each segmentation candidates (SCs) in the segmentation graph. Then, using concatenated letter-HMMs, a likelihood is computed for each word in the lexicon by multiplying the probabilities over the best paths through the graph. We present in detail two approaches to train the word recognizer: 1). character-level training 2). word-level training. The recognition performances of the two systems are discussed. I.

—Face recognition has become an important subject in modern life, especially in security and surveillance applications. This work introduces a face recognition method, which is characterized by high-speed, low-complexity, and high-efficiency in a noisy environment. The performance of this method is greatly improved by using a median filter, such that each image is filtered to eliminate the influence of noise and light illumination. Multiple levels of discrete wavelet transform are applied to the filtered image to reduce size and eliminate further noise. Subsequently, the resultant image is scanned using a window with a predefined overlap in raster fashion to construct a sequence of observation vectors used as the basis of a model. The model consists of two states of a continuous hidden Markov model, a unique model in the face recognition field that has not yet been used by other researchers, which interprets the novelty and low complexity of the method. In spite of the presence of 0.15, 0.4, and 0.25 measurement values of impulse noise density in images stored in the ORL, Yale, and EURECOM Kinect face databases, respectively, the proposed work has achieved a recognition rate of 100%. I. INTRODUCTION Face recognition has recently received considerable attention due to its important applications in many areas and fields such as credit card verification, security, and surveillance. The advantage of face recognition over other biometric techniques is the mechanism to recognize persons, which is frequently performed without requiring that the person have physical contact with the device, e.g. use of palm or fingers. The face image can be captured by a camera without informing the target person, especially in the case of criminal surveillance and apprehension. The methods introduced to recognize faces vary relative to the procedure used for feature extraction and type of classifier used. The artificial neural network [1], principle component analysis (PCA) [2], independent component analysis [3], linear discriminant analysis (LDA) [4], support vector machine [5] and K-nearest neighbour [6] are the most widely used methods. In addition, the hidden Markov model (HMM) [7] has been successfully used in face recognition during the last two decades. The advantage of applying HMM to face recognition is the flexibility of the selection of training models, such that it is easy to add or remove individuals. There is no need for retraining the overall system; however, a model with updated images must be retrained. For dimensionality reduction, a particular feature extraction method is applied to form a sequence of observation vectors to model faces. Thereafter, for each individual, one HMM model is constructed by training a specified set of images for that individual. The training process depends on the estimation of the HMM parameters [8], such that the process is iterated several times before it converges. In the testing process, the algorithm computes the probability of the observations of unknown face image calibrated with the parameters derived from the training. The unknown face belongs to the person whose model computes the highest probability. The disadvantages of using more states of the HMM are: high computational complexities, large memory requirements, and low processing speed. The proposed work addresses the unfavourable features by using only two states of the HMM.

In Natural Language Processing (NLP), Word segmentation and Part-of-Speech (POS) tagging are fundamental tasks. The POS information is also necessary in NLP's preprocessing work applications such as machine translation (MT), information retrieval (IR), etc. Currently, there are many research efforts in word segmentation and POS tagging developed separately with different methods to get high performance and accuracy. For Myanmar Language, there are also separate word segmentors and POS taggers based on statistical approaches such as Neural Network (NN) and Hidden Markov Models (HMMs). But, as the Myanmar language's complex morphological structure, the OOV problem still exists. To keep away from error and improve segmentation by utilizing POS data, segmentation and labeling should be possible at the same time.The main goal of developing POS tagger for any Language is to improve accuracy of tagging and remove ambiguity in sentences due to language structure. This paper focuses on developing word segmentation and Part-of-Speech (POS) Tagger for Myanmar Language. This paper presented the comparison of separate word segmentation and POS tagging with joint word segmentation and POS tagging.

This paper presents the design of a FPGA-based hardware co-processor, based on the SPHINX 3 speech recognition engine from CMU; capable of performing Acoustic Modeling (AM) for medium sized vocabularies in real-time. By creating an input-driven pipeline for performing the ...

In this work, we provide a variational Bayesian (VB) treatment of multistream fused hidden Markov models (MFHMMs), and we apply it in the context of active learning-based visual workflow recognition. Contrary to training methods yielding point estimates, such as maximum likelihood or maximum a posteriori training, the VB approach provides an estimate of the posterior distribution over the MFHMM parameters. As a result, our approach provides an elegant solution towards the amelioration of the overfitting issues of point estimate-based methods. Additionally, it provides a measure of confidence in the accuracy of the learned model, thus allowing for the easy and cost-effective utilization of active learning in the context of MFHMMs. Two alternative active learning algorithms are considered in this paper: query by committee, which selects unlabeled data that minimize the classification variance, and a maximum information gain method which aims to maximize the alteration in model variance by proper data labeling. We demonstrate the efficacy of the proposed treatment of MFHMMs by examining two challenging workflow recognition scenarios, and we show that the application of active learning, which is facilitated by our VB approach, allows for a significant reduction of the MFHMM training costs.

The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.

Online auction, shopping, electronic billing etc. all such types of application involves problems of fraudulent transactions. Online fraud occurrence and its detection is one of the challenging fields for web development and online phantom transaction. As no-secure specification of online frauds is in research database, so the techniques to evaluate and stop them are also in study. We are providing an approach with Hidden Markov Model (HMM) and mobile implicit authentication to find whether the user interacting online is a fraud or not. We propose a model based on these approaches to counter the occurred fraud and prevent the loss of the customer. Our technique is more parameterized than traditional approaches and so,chances of detecting legitimate user as a fraud will reduce.

Abstract. In RoboCup Middle Size league (MSL) the main referee uses assisting technology, controlled by a second referee, to support him, in particular for conveying referee decisions for robot players with the help of a wireless communication system. In this paper a vision-based system is introduced, able to interpret dynamic and static gestures of the referee, thus eliminating the need for a second one. The referee's ges-tures are interpreted by the system and sent directly to the Referee Box, which sends the proper commands to the robots. The system is divided into four modules: a real time hand tracking and feature extraction, a SVM (Support Vector Machine) for static hand posture identification, an HMM (Hidden Markov Model) for dynamic unistroke hand gesture recognition, and a FSM (Finite State Machine) to control the various system states transitions. The experimental results showed that the system works very reliably, being able to recognize the combination of gestures a...

Verbmobil, a German research project, aims at machine translation of spontaneous speech input. The ultimate goal is the development of a portable machine translator that will allow people to negotiate in their native language. Within this project the University of Karlsruhe has developed a speech recognition engine that has been evaluated on a yearly basis during the project and shows very promising speech recognition word accuracy results on large vocabulary spontaneous speech. We introduce the Janus Speech Recognition Toolkit underlying the speech recognizer. The main new contributions to the acoustic modeling part of our 1996 evaluation system-speaker normalization, channel normalization and polyphonic clustering-are discussed and evaluated. Besides the acoustic models we delineate the different language models used in our evaluation system: word trigram models interpolated with class based models and a separate spelling language model were applied. As a result of using the toolk...

Cel pracy: 1) zbadanie wpływu różnorodnych czynników na skuteczność i szybkość rozpoznawania izolowanych słów mowy polskiej; 2) opracowanie efektywnej metody rozpoznawania izolowanych słów polskich. Zbadano wpływ częstotliwości próbkowania sygnału mowy oraz liczby poziomów kwantyzacji na efektywność niezależnego i zależnego od mówcy rozpoznawania izolowanych wypowiedzi polskich z bazy CORPORA za pomocą metody nieliniowej transformacji czasowej (DTW) oraz niejawnych modeli Markowa HMM. Wykazano istnienie optimum doboru częstotliwości próbkowania oraz liczby poziomów kwantyzacji. Zbadano również częściowo zależność rozpoznawania izolowanych wypowiedzi od płci mówcy, wielkości zbioru uczącego oraz wybranych parametrów niejawnych modeli Markowa. Podniesiono skuteczność rozpoznawania izolowanych wypowiedzi polskich za pomocą metody nieliniowej transformacji czasowej poprzez zastosowanie wstępnej klasyfikacji za pomocą: 1) znaczników fonetycznych; 2) niejawnych modeli Markowa.

Usage monitoring entails determining the actual usage of a component on the aircraft and requires accurate recognition of regimes. In this paper, a data mining approach is adopted for regime recognition. In particular, a regime recognition algorithm developed based on hidden Markov models is presented. The developed algorithm was validated using the flight card data of an Army UH-60L helicopter. The performance of this regime recognition algorithm was also compared with other data mining methods using the same dataset. Using the flight card information and regime definitions, a dataset of about 56,000 data points labeled with 50 regimes recorded in the flight card were mapped to the health and usage monitoring parameters. The validation and performance comparison results have showed that the hidden Markov model based regime recognition algorithm was able to accurately recognize the regimes recorded in the flight card data and outperformed other data mining methods.

livre reconnaisance et apprentissage des formes et du son : b preface, shrikanth narayanan and abeer alwan (editors). /bb foreword:modification of speech: tribute to mike macon, jan van santen. ... Sommaire. Preface, Shrikanth Narayanan and Abeer Alwan (Editors). ...

Traffic congestion is an important socioeconomic problem that swelled in the last few decades. It affects the social mobility of people, length of trips, quality of life, and the economy of countries. As a major problem in most countries, it has been tackled by governments, universities, and advanced research using intelligent transportation systems (ITS) to solve the problem or at least ease its adverse effects. Hidden Markov Models (HMM) represent one of the methods that are suitable for congestion prediction. In this paper, a new model, based on Hidden Markov Model and Contrast, is proposed to define the traffic states during peak hours in two dimensional space (2D). The proposed model uses mean speed and contrast to capture the variability in traffic patterns. Empirical evaluation shows that the proposed approach has improved prediction error in comparison to HMM related work and neuro-fuzzy approaches. Ó 2019 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).