Uncovering cognitive influences on individualized learning using a hidden Markov models framework

In recent years, automatic map matching has received great technical progress. However, when it comes to vague matching situations such as improper vocabulary use, there still lack reliable solutions. To handle the current gap, this paper proposes a novel automatic map matching method based on the hybrid computing framework of hidden Markov model (HMM) and conditional random field. First, the data filtering is completed by performing second-order transformation towards automatic matching conditions of HMM. Then, the data classification is completed using automatic data classification based on the conditional random field. After that, a hybrid computing framework with spatial elements and layer selection is built to generate map matching results. Finally, some simulation experiments are conducted for evaluation. For one thing, the trend of matching accuracy changes under specified conditions is basically the same as that of nonspecified conditions. The maximum difference in matching calculation values is about 3 times. However, once the vocabulary continues to increase, the difference in matching results between the two narrows to 10–20%. For the other thing, the matching accuracy of a specified state is higher than that of sending a specified state. While nonspecified fuzzy matching accuracy is about 3 times higher and nonspecified precision matching accuracy is about 50% higher.

The main mission of the intelligent tutoring system is to estimate and predict the students’ learning status accurately and to generate a learning path that is inherent to the students. There are many learning state estimation and prediction methods, and there are also many methods to generate learning paths using the results. We propose a method to predict the learning state of a student and generate a learning path using the Hidden Markov Model, a probabilistic reasoning over time. We propose and illustrate in detail how to formalize the Hidden Markov Model using an extended knowledge graph for learning state prediction, how to collect the evidence variable values of the Hidden Markov Model in intelligent tutoring systems, how to predict the learning state using smoothing algorithms, and how to generate the stochastic learning path. The experiment was conducted to compare students using an intelligent tutoring system with those who did not. In addition, a comparison experiment of a method using Hidden Markov model with a multi-dimensional linear prediction model such as PFM and AFM was also carried out. The results showed that the average performance of students using the intelligent tutoring system was much higher than that of students without the intelligent tutoring system. In addition, the experimental results showed that the average performance of students using Hidden Markov models was higher than that of students using other models.

This study is designed to investigate the feasibility of automatic sleep staging using features only derived from electrocardiography (ECG) signal. The study was carried out using the framework of hidden Markov models (HMMs). The mean, and SD values of heart rates (HRs) computed from each 30-second epoch served as the features. The two feature sequences were first detrended by ensemble empirical mode decomposition (EEMD), formed as a two-dimensional feature vector, and then converted into code vectors by vector quantization (VQ) method. The output VQ indexes were utilized to estimate parameters for HMMs. The proposed model was tested and evaluated on a group of healthy individuals using leave-one-out cross-validation. The automatic sleep staging results were compared with PSG estimated ones. Results showed accuracies of 82.2%, 76.0%, 76.1% and 85.5% for deep, light, REM and wake sleep, respectively. The findings proved that HRs-based HMM approach is feasible for automatic sleep staging and can pave a way for developing more efficient, robust, and simple sleep staging system suitable for home application.

Proposes a global acoustic segment model for characterizing fundamental speech sound units and their interactions based upon a general framework of hidden Markov models (HMM). Each segment model represents a class of acoustically similar sounds. The intra-segment variability of each sound class is modeled by an HMM, and the sound-to-sound transition rules are characterized by a probabilistic intersegment transition matrix. An acoustically-derived lexicon is used to construct word models based upon subword segment models. The proposed segment model was tested on a speaker-trained, isolated word, speech recognition task with a vocabulary of 1109 basic English words. In the current study, only 128 segment models were used, and recognition was performed by optimally aligning the test utterance with all acoustic lexicon entries using a maximum likelihood Viterbi decoding algorithm. Based upon a database of three male speakers, the average word recognition accuracy for the top candidate was 85% and increased to 96% and 98% for the top 3 and top 5 candidates, respectively. >

This paper describes a method for improving handwritten word recognition by implicitly recognizing the writing style. Writing style is taken here to cover several types of distinctions between word shapes: cursive script vs. handprinted words, run-on vs. discrete words, differences in skew angle values, stability of lower and upper extensions of letters, presence or absence of loops in naturally looped letters. This method is applied in the general framework of hidden Markov models (HMM). The proposed method consists in the use of a set of models rather than of a unique model for each word. Each model in the set is associated to a certain class of writers, As a consequence, writing style is automatically and implicitly detected during recognition. But the main contribution of the method is a better estimation of the recognition score, i.e. the probability of generating the word with the HMM.

Multivariate time series anomaly detection: A framework of Hidden Markov Models

In this letter, we propose a new trajectory model for characterizing segmental features and their interaction based upon a general framework of hidden Markov models. Each segment, a sequence of frame vectors, is represented by a trajectory of observed vector sequences. This trajectory replaces the frame features in the segment and becomes the input of the segmental hidden Markov models (HMM's). In our approach, we adopt polynomial trajectory modeling to represent the trajectories using a new design matrix that includes transitional information on neighborhood acoustic events. To apply this trajectory to the segmental HMM, extra- and intrasegmental variations are modified to contain trajectory information. The presented model is regarded as an extension and generalization of conventional HMM, trajectory-based segmental HMM, and parametric trajectory models. The experimental results are reported on the TIMIT corpus and performance is shown to improve significantly over that of the conventional HMM.

A perceptually plausible solution to the problem of automatic recognition of speech in arbitrary noise backgrounds involves computational auditory scene analysis (ASA) followed by recognition of the separated patterns. However, it is not generally possible to recover a complete representation of individual acoustic sources, so a new approach is required to recognize partial descriptions. Suitable modifications to the powerful stochastic framework of hidden Markov models (HMM) have recently been described [M. P. Cooke, P. D. Green, and M. D. Crawford, Proc. Int. Conf. Spoken Language Processing (1994)]. The studies reported here demonstrate HMM-based digit recognition in noise. An auditory-nerve firing rate representation undergoes auditory scene analysis, producing a mask of time-frequency locations where the speech is dominant. Each mask frame defines a marginal distribution for the HMM probability calculation. Results show robust performance even when the mask has most of its elements removed. Further, these studies suggest a solution to the problem of sensitivity to F0 in matching auditory representations of speech in which F1 is represented by a set of resolved harmonics. The new approach ensures that the matching process operates on a partial description consisting largely of harmonic peaks.

We consider distributed parameter estimation using quantized observations in wireless sensor networks (WSN) operating in a noisy channel environment. Due to bandwidth constraints, each sensor quantizes its local observation into one bit of information. Previously, adaptive quantization(AQ) schemes were developed under the assumption of perfect communication links between the sensors and the fusion center (FC). In this paper we propose an adaptive quantization scheme for a WSN with channel links modeled as binary erasure channels. A first-order Hidden Markov Model (HMM) framework is introduced to model the adaptive quantization scheme. The introduction of a HMM framework aids in the systematic design of an estimator. To address the significant problem of bit erasures, we propose an Expectation-Maximization (EM) based estimator. Theoretical closed form solutions for the Cramer-Rao lower bounds are developed for the proposed estimation problem under certain assumptions. We analyze the performance of the proposed quantization scheme and estimator under different criteria. Numerical simulation results are shown for the proposed adaptive quantization and EM parameter estimation scheme under different scenarios. The simulation results indicate that the proposed quantization scheme and estimator are robust and can provide superior performance for erasure rates up to 10 %.

The ProtoLOGOS Romanian language automatic speech recognition and understanding system is based at the level of acoustical modeling on the statistical framework of hidden Markov models and at the linguistic level on a finite state grammar or a bigram language model. The system enhanced the speech signal modeling by using for the first time the PLP (Perceptual Linear Prediction) features in a Romanian language recognition system. The main performance indicator for our system is the phrase recognition rate. This paper presents the main results obtained using the two language models already mentioned and the METEO database, constituted by broadcasted forecast news records.

The rapid advance in mobile communications has made information and services ubiquitously accessible. Location and context information have become essential for the effectiveness of services in the era of mobility. This paper proposes the concept of geo-context that is defined as an integral synthesis of geographical location, human motion state and mobility context. A geo-context computing solution consists of a positioning engine, a motion state recognition engine, and a context inference component. In the geo-context concept, the human motion states and mobility context are associated with the geographical location where they occur. A hybrid geo-context computing solution is implemented that runs on a smartphone, and it utilizes measurements of multiple sensors and signals of opportunity that are available within a smartphone. Pedestrian location and motion states are estimated jointly under the framework of hidden Markov models, and they are used in a reciprocal manner to improve their estimation performance of one another. It is demonstrated that pedestrian location estimation has better accuracy when its motion state is known, and in turn, the performance of motion state recognition can be improved with increasing reliability when the location is given. The geo-context inference is implemented simply with the expert system principle, and more sophisticated approaches will be developed.

Recent research on biomedical signal processing especially ECG analysis mostly focused on the use of Hidden Markov Models (HMM). The general aim of any signal segmentation method is to partition a given signal into consecutive regions of interest. In the context of the ECG then, the role of segmentation is to determine as accurately as possible the onset and offset boundaries, as well as the peak locations, of the various waveform features, such that the ECG interval measurements may be computed automatically and the study of waveform patterns will be facilitated (Sayadi & Shamsollahi, 2009). Ad hoc algorithms have been developed in order to help cardiologists to segment large amounts of ECGs. But these algorithms do not provide a precise segmentation, and repetitive corrections have to be made. Wavelet parametrisation is known to highlight discontinuities in the signal, and has proven to give good results for ECG segmentation (Kawaja et al., 2006; Thomas et al., 2006). A statistical model helps to regularize the detection, resulting in a more robust delineation. One of the advantages of probabilistic models over traditional methods is that a confidence measure for each segmented signal is given by the log likelihood of the observed signal given the model (Thomas et al., 2006). The main focus of this chapter is to introduce some new and robust HMM associated with wavelet transform based methods for ECG analysis. The chapter begins with a review of the literature on the use of HMM to analyse ECG signals. We then consider in detail the suitability of HMM to provide a faithful statistical description of the ECG. In particular, we examine the validity of the various assumptions inherent in the HMM framework in the context of the ECG. Following this, we consider a number of specific issues in developing an HMM for ECG segmentation, including the choice of model architecture, type of observation models. Then a combination of HMM approach with wavelet properties will be explained.

This paper proposes an approach combining the set of Krawtchouk moments and HMMs (Hidden Markov Models) for unconstrained offline handwritten Chinese character recognition. The Krawtchouk moments can be effectively used as pattern features in analysis of two-dimensional images. Since the basis set is orthogonal in the discrete domain of the image coordinate space, the implementation of Krawtchouk moments doesn't involve any numerical approximation or coordinate transformation. This property makes Krawtchouk moments superior to conventional, more continuous moments such as Legendre moments and Zernike moments. In the field of character recognition, HMMs architecture has been used widely because of its strong adaptability to written distortion and variability. In this paper, Krawtchouk moments and HMMs are combined to recognize unconstrained offline handwritten Chinese amount in words. Experiments show that the average recognition of Krawtchouk is higher than Zernike and Legendre moments within the HMM framework.

Hidden Markov models (HMMs) are widely used to characterize latent state transition patterns in substance use. However, traditional HMM frameworks are incompetent when dealing with the complexities introduced by high-dimensional risk factors and varying time intervals, particularly in determining the number of hidden states and selecting variables for state transition parameters. To tackle the analytical challenges in the Population Assessment of Tobacco and Health (PATH) Study, a nationally representative longitudinal cohort study on tobacco use, we propose a continuous-time HMM framework with a regularization algorithm to identify multi-dimensional risk factors underlying complex poly-tobacco use transitions. We develop an elastic-net regularization on the transition covariates to identify informative covariates and improve model estimation accuracy. The inclusion of key covariates enables accurate determination of the number of hidden states. We incorporate survey weights and information on strata and clustering throughout the modeling framework. We demonstrate the validity of our approach in determining state numbers, identifying informative covariates, and estimating model parameters through a series of simulations. Application of the proposed approach to PATH data analysis revealed several demographic, behavioral, and psychosocial factors that contribute to the differential risks of transition between tobacco-use states among youth and young adults. The model's capacity in identifying high-dimensional risk factors for underlying hidden variables substantiates its potential for enhancing public health research and informing interventions.

Despite their known weaknesses, hidden Markov models (HMMs) have been the dominant technique for acoustic modeling in speech recognition for over two decades. Still, the advances in the HMM framework have not solved its key problems: it discards information about time dependencies and is prone to overgeneralization. In this paper, we attempt to overcome these problems by relying on straightforward template matching. The basis for the recognizer is the well-known DTW algorithm. However, classical DTW continuous speech recognition results in an explosion of the search space. The traditional top-down search is therefore complemented with a data-driven selection of candidates for DTW alignment. We also extend the DTW framework with a flexible subword unit mechanism and a class sensitive distance measure-two components suggested by state-of-the-art HMM systems. The added flexibility of the unit selection in the template-based framework leads to new approaches to speaker and environment adaptation. The template matching system reaches a performance somewhat worse than the best published HMM results for the Resource Management benchmark, but thanks to complementarity of errors between the HMM and DTW systems, the combination of both leads to a decrease in word error rate with 17% compared to the HMM results