Conventional Hidden Markov Model Research Articles

Protocol-based SMC for singularly perturbed systems with switching parameters and deception attacks

This research paper investigates a protocol-based sliding mode control (SMC) approach for singularly perturbed systems with switching parameters and random occurring deception attacks. The mode switching behavior of the original system is regulated using a semi-Markov process, and a novel and more efficient mode monitoring method is proposed, distinct from the conventional hidden Markov model. Furthermore, to reduce the triggering rate while maintaining control performance, a probability-based event triggering protocol is introduced. By employing the proposed scheme along with Lyapunov theory, sufficient criteria for parameter correlation are established to ensure asymptotic stability. The designed sliding mode control incorporates a convergence factor to enhance the control performance. Finally, the feasibility and practicality of the proposed control strategy are verified through simulations using two practical models.

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human–computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM’s uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.

Bayesian Heterogeneous Hidden Markov Models with an Unknown Number of States

Hidden Markov models (HMMs) are valuable tools for analyzing longitudinal data due to their capability to describe dynamic heterogeneity. Conventional HMMs typically assume that the number of hidden states (i.e., the order of HMMs) is known or predetermined through criterion-based methods. However, prior knowledge about the order is often unavailable, and a pairwise comparison using criterion-based methods becomes increasingly tedious and computationally demanding when the model space enlarges. A few studies have considered simultaneously performing order selection and parameter estimation under the frequentist framework. Still, they focused only on homogeneous HMMs and thus cannot accommodate situations where potential covariates affect the between-state transition. This study proposes a Bayesian double-penalized (BDP) procedure to conduct a simultaneous order selection and parameter estimation for heterogeneous HMMs. We develop a novel Markov chain Monte Carlo algorithm coupled with an efficient adjust-bound reversible jump scheme to address the challenges in updating the order. Simulation studies show that the proposed BDP procedure considerably outperforms the commonly used criterion-based methods. An application to the Alzheimer’s Disease Neuroimaging Initiative study further confirms the utility of the proposed method. Supplementary materials for this article are available online.

Spatially Weighted Bayesian Classification of Spatio-Temporal Areal Data Based on Gaussian-Hidden Markov Models

This article is concerned with an original approach to generative classification of spatiotemporal areal (or lattice) data based on implementation of spatial weighting to Hidden Markov Models (HMMs). In the framework of this approach data model at each areal unit is specified by conditionally independent Gaussian observations and first-order Markov chain for labels and call it local HMM. The proposed classification is based on modification of conventional HMM by the implementation of spatially weighted estimators of local HMMs parameters. We focus on classification rules based on Bayes discriminant function (BDF) with plugged in the spatially weighted parameter estimators obtained from the labeled training sample. For each local HMM, the estimators of regression coefficients and variances and two types of transition probabilities are used in two levels (higher and lower) of spatial weighting. The average accuracy rate (ACC) and balanced accuracy rate (BAC), computed from confusion matrices evaluated from a test sample, are used as performance measures of classifiers. The proposed methodology is illustrated for simulated data and for real dataset, i.e., annual death rate data collected by the Institute of Hygiene of the Republic of Lithuania from the 60 municipalities in the period from 2001 to 2019. Critical comparison of proposed classifiers is done. The experimental results showed that classifiers based on HMM with higher level of spatial weighting in majority cases have advantage in spatial–temporal consistency and classification accuracy over one with lower level of spatial weighting.

Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements.

It is important to find signals of interest (SOIs) when operating sonar systems. A threshold-based method is generally used for SOI detection. However, it induces a high false alarm rate at a low signal-to-noise ratio. On the other side, machine-learning-based detection is performed to obtain more reliable detection results using abundant training data, costing intensive time and labor. We propose a method with favorable detection performance by using a hidden Markov model (HMM) for sequential acoustic data, which requires no separate training data. Since the detection results from HMM are significantly affected by the random initial parameters of HMM, the genetic algorithm (GA) is adopted to reduce the sensitivity of the initial parameters. The tuned initial parameters from GA are used as a start point for the subsequent Baum–Welch algorithm updating the HMM parameters. Furthermore, multiple measurements from arrays are exploited both in determining the proper initial parameters with GA and updating the parameters with the Baum–Welch algorithm. In contrast to the standard random selection of the initial point with single measurement, a stable initial point setting by the GA ensures improved SOI detections with the Baum–Welch algorithm using the multiple measurements, which are demonstrated in passive and active acoustic data. Particularly, the proposed method shows the most confidential detection in finding weak elastic surface waves from target, compared to existing methods such as conventional HMM.

An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations

With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common. Residual charts are a good approach to monitoring the process with data autocorrelation. An improved hidden Markov model (IHMM) for the prediction of autocorrelated observations and a new expectation maximization (EM) algorithm is proposed. A residual chart based on IHMM is employed to monitor the autocorrelated process. The numerical experiment shows that, in general, IHMMs outperform both conventional hidden Markov models (HMMs) and autoregressive (AR) models in quality shift diagnosis, decreasing the cost of missing alarms. Moreover, the times taken by IHMMs for training and prediction are found to be much less than those of HMMs.

Simultaneous positive sequential vectors modeling and unsupervised feature selection via continuous hidden Markov models

Since positive data vectors are often naturally generated in various real-life applications, positive vectors modeling has become an important research topic. In this article, we tackle the problem of modeling positive sequential vectors through continuous hidden Markov models (HMMs). Motivated by several recent studies in which the generalized inverted Dirichlet (GID) distribution has provided better performance than the Gaussian distribution for modeling positive data, instead of adopting Gaussian mixture models (GMM) as the emission density for conventional continuous HMMs, we theoretically propose a novel HMM by considering the mixture of GID distributions as the emission density. Moreover, to cope with high-dimensional data which may contain irrelevant features, an unsupervised localized feature selection method is incorporated with our model, which results in a unified framework that can simultaneously perform positive sequential data modeling and feature selection. To learn the proposed model, we develop a convergence-guaranteed algorithm based on variational Bayes. The advantages of our model are demonstrated through both simulated data sets and a real-life application about human action recognition.

Multi-view representation based speech assisted system for people with neurological disorders

Ambient assisted living technology makes use of smart devices to enhance the well-being of elder or disabled persons in their daily life. Assistive technology for people with neurological disorders gains importance and is a challenging task. Developing an intelligent speech assistive system for such patients is a kind of rehabilitation to improve their quality of life. One kind of neurological disorder is dysarthria, a motor speech impairment which causes articulatory deficits. People with speech impairment have difficulty in pronouncing words, which results in variations in phonemes, leading to speech impairment. To capture these variations effectively from impaired speech utterances, we propose a multi-view representation based disordered speech recognition system. Auditory images generated for the impaired speech utterances of different word classes are well discriminative in nature. Multi-view representations are formed using the auditory image-based features and cepstral features. The proposed multi-view representations provide better discrimination for acoustically similar but different word classes. The proposed approach is evaluated using the UA-SPEECH corpus with two different datasets, namely 100 common words and 15-word classes of acoustically similar pairs of words. The training and testing samples are collected at varied intelligibility levels such as high, medium, low, and very low. Compared with conventional hidden Markov models (HMM), deep neural network-HMM, constant-Q-transform (CQT) and other single view auditory image-based approaches, the proposed approach proves that even the very low intelligibility words are recognized properly, leading to improved performance of disordered speech recognition.

An HDP-HMM based approach for tool wear estimation and tool life prediction

Tool wear estimation and prediction are keys of maintenance decision-making for milling machine. Various discrete-state degradation models have been developed for tool wear estimation and prediction. However, previous research assume that the number of discrete wear states is fixed based on prior understanding of tool degradation process. To break this limitation, a data-driven approach based on Hierarchical Dirichlet process-Hidden Markov model (HDP-HMM) is proposed. The number of states, transition probability matrix and omission probability distribution of hidden Markov model (HMM) can be automatically updated using observation data through a hierarchical Dirichlet process (HDP). Compared with weighted HMM and Conventional HMM, experiments on real data from high-speed CNC milling machine cutters demonstrates that the proposed approach yielded greater accuracy on tool wear estimation and kept a high reliability in tool life prediction.

Robust algorithm for attack detection based on time‐varying hidden Markov model subject to outliers

SummaryThe problem of robust attack detection and prediction for networked control systems in the presence of outliers is discussed in this article. The conventional hidden Markov model (HMM) is trained to learn the system behavior (ie, transitions between different operating modes) in the nominal process. The HMM with time‐varying transition probabilities is used to track the attack behavior in which the adversary triggers more hazard modes to hasten fatigue of control devices by injecting attack signals with random magnitude and frequency. For different operating modes, the observations are assumed to follow different multivariate Student's t distributions instead of Gaussian distributions and thus address the robust estimation problem. The expectation maximization algorithm is used to estimate parameters. Finally, simulations are conducted to verify the effectiveness of the proposed method.

From pup to predator: generalized hidden Markov models reveal rapid development of movement strategies in a naïve long‐lived vertebrate

Rapid development of a successful foraging strategy is critical for juvenile survival, especially for naïve animals that receive no parental guidance. However, this process is poorly understood for many species. Although observation of early‐life movements is increasingly possible with miniaturisation of animal‐borne telemetry devices, analytical limitations remain. Here, we tracked 29 recently‐weaned grey seal Halichoerus grypus pups from colonies in two geographically distinct regions of the United Kingdom. We analysed at‐sea movements of pups throughout their initial months of nutritional independence to investigate the ontogeny of behaviour‐specific (foraging and travelling) movement patterns. Using generalized hidden Markov models (HMMs), we extended the conventional HMM framework to account for temporal changes in putative foraging and travelling movement characteristics, and investigate the effects of intrinsic (sex) and extrinsic (environment) factors on this process. Putative foraging behaviour became more tortuous with time, and travelling became faster and more directed, suggesting a reduction in search scale and an increase in travel efficiency as pups shifted from exploration to an adult‐like repeatable foraging strategy. Sex differences in movement characteristics were evident from colony departure, but sex‐specific activity budgets were only detected in one region. We show that sex‐specific behavioural strategies emerge before sexual size dimorphism in grey seals, and suggest that this phenomenon may occur in other long‐lived species. Our results also indicate that environmental variation may affect the emergence of sex‐specific foraging behaviour, highlighting the need to consider interacting intrinsic and extrinsic factors in shaping movement strategies of long‐lived vertebrates. Moreover, comparing the behavioural state estimations to those of a conventional HMM (no variation in state‐specific movement parameters) revealed differences in the amount and location of foraging activity, with implications for spatial conservation management. Overlooking intrinsic and extrinsic variation in movement processes could distort our understanding of foraging ecology, population dynamics and conservation requirements.

Ensemble Gaussian mixture model-based special voice command cognitive computing intelligent system

Dysarthria is a speech disorder caused by stroke, Parkinson’s disease, neurological injury, or tumors that damage the nervous system and weaken the speech quality. Developing a unique voice command system for Dysarthric speech helps to recognize impaired speech and convert them into text or input commands. Hidden Markov Model (HMM) is one of the widely used generative model-based classifiers for Dysarthric speech recognition. But due to insufficient training data, HMM doesn’t provide optimal results on overlapping classes. We propose an ensemble Gaussian mixture model to recognize impaired speech more accurately. Our model converts the sequence of feature vectors into a fixed dimensional representation of patterns with varying lengths. The performance efficiency of the proposed model is evaluated on the Dysarthric UA-speech benchmark dataset. The discriminatory information provided by the proposed approach yields better classification accuracy even for shallow intelligibility words compared to conventional HMM.

Positive Sequential Data Modeling Using Continuous Hidden Markov Models Based on Inverted Dirichlet Mixtures

The hidden Markov model (HMM) has long been one of the most commonly used probability graph models for modeling sequential or time series data. It has been widely used in many fields ranging from speech recognition, face recognition, anomaly detection, to gene function prediction. In this paper, we theoretically propose a variant of the continuous HMM for modeling positive sequential data which are naturally generated in many real-life applications. In contrast with conventional HMMs which often use Gaussian distributions or Gaussian mixture models as the emission probability density, we adopt the inverted Dirichlet mixture model as the emission density to build the HMM. The consideration of inverted Dirichlet mixture model in our case is motivated by its superior modeling capability over Gaussian mixture models for modeling positive data according to several recent studies. In addition, we develop a convergence-guaranteed approach to learning the proposed inverted Dirichlet-based HMM through variational Bayes inference. The effectiveness of the proposed HMM is validated through both synthetic data sets and a real-world application regarding anomaly network intrusion detection. Based on the experimental results, the proposed inverted Dirichlet-based HMM is able to achieve the detection accuracy rates that are about 4%~9% higher than those ones obtained by the compared approaches.

Exploration of Properly Combined Audiovisual Representation with the Entropy Measure in Audiovisual Speech Recognition

Deep belief networks (DBNs) have shown impressive improvements over the Gaussian mixture models whilst are employed inside the Hidden Markov Model (HMM)-based automatic speech recognition systems. In this study, the benefits of the DBNs to be used in audiovisual speech recognition systems are investigated. First, the DBN-HMMs are explored in speech recognition and lip-reading tasks, separately. Next, the challenge of appropriately integrating the audio and visual information is studied; for this purpose, the application of the fused feature in an audiovisual (AV) DBN-HMM based speech recognition task is studied. With regard to the integration of information, those layers that provide generalities and details with together, so that in overall a completion is made, are selected. A modified technique is proposed based on the entropy of different layers of the used DBNs, to measure the amount of information. The best audio layer representation is found to have the highest entropy, with the highest power of providing information details in the fusion scheme. In contrast, the best visual layer representation is found to have the lowest entropy, which could best provide sufficient generalities. Over the CUAVE database, on English digit recognition task, the conducted experiments show that the AV DBN-HMM, with proposed feature fusion method, can reduce phone error rate by as much as 4% and 1.5%, and word error rate by about 3.49% and 1.89%, over the baseline conventional HMM and audio DBN-HMM, respectively.

Dynamic Hand Gesture Recognition Using LMC for Flower and Plant Interaction

As the recent novel somatosensory devices become more pervasive, dynamic hand gesture recognition algorithm has attracted substantial research attention and has been widely used in the area of human–computer interaction (HCI). This paper aims to develop low-complexity and real-time solutions of dynamic hand gesture recognition using Leap Motion Controller (LMC) for flower and plant interactive applications. In this paper, we use two LMCs to obtain gesture data from different angles for fusion processing and then propose a novel feature vector, which adapts to representing dynamic hand gestures. After this, an improved Hidden Markov Model (HMM) algorithm was proposed to obtain the final recognition results, in which we apply the Particle Swarm Optimization (PSO) to avoid the complex computation of parameters in conventional HMM, thus improving the recognition performance. The experimental results on test datasets demonstrate that the proposed algorithm can achieve a higher average recognition rate of 96.5% for Leap-Gesture and 97.3% for Manipulation-Gesture. In addition, through the experiment of a flower and plant interaction, our dynamic gesture recognition solution can help users realize the interactive operation accurately and efficiently. In contrast to previous studies, our prototype system provides the users with a new dimension of experience and changes the research model of traditional forestry.

Gating Neural Network for Large Vocabulary Audiovisual Speech Recognition

Audio-based automatic speech recognition (A-ASR) systems are affected by noisy conditions in real-world applications. Adding visual cues to the ASR system is an appealing alternative to improve the robustness of the system, replicating the audiovisual perception process used during human interactions. A common problem observed when using audiovisual automatic speech recognition (AV-ASR) is the drop in performance when speech is clean. In this case, visual features may not provide complementary information, introducing variability that negatively affects the performance of the system. The experimental evaluation in this study clearly demonstrates this problem when we train an audiovisual state-of-the-art hybrid system with a deep neural network (DNN) and hidden Markov models (HMMs). This study proposes a framework that addresses this problem, improving, or at least, maintaining the performance when visual features are used. The proposed approach is a deep learning solution with a gating layer that diminishes the effect of noisy or uninformative visual features, keeping only useful information. The framework is implemented with a subset of the audiovisual CRSS-4ENGLISH-14 corpus which consists of 61 h of speech from 105 subjects simultaneously collected with multiple cameras and microphones. The proposed framework is compared with conventional HMMs with observation models implemented with either a Gaussian mixture model or DNNs. We also compare the system with a multi-stream HMM system. The experimental evaluation indicates that the proposed framework outperforms alternative methods under all configurations, showing the robustness of the gating-based framework for AV-ASR.

Global statistical features-based approach for Acoustic Event Detection

The analysis of acoustic data typically discusses the problem of segmenting the acoustic events into non-overlapping acoustically compact categories. In Acoustic Event Detection (AED), an acoustic event is categorized into speech and non-speech events. Detection of non-speech sounds such as scream, gun shots, explosions, and glass break events is very helpful in acoustic surveillance, multimedia information retrieval, and acoustic forensic applications. In this paper, we propose global statistical features-based representation for multi-variate varying length acoustic data. A discriminative model-based classifier is then used to classify different acoustic events. The proposed representation is of very less dimension. The proposed approach is evaluated on surveillance-oriented AED datasets such as CICESE (recorded from a smart room scenario), Environmental Sound Classification (ESC), and IEEE AASP/DCASE2013 (Office environment) datasets. The proposed approach gives a better performance when compared with the conventional Hidden Markov Model (HMM) and Gaussian Mixture Model (GMM) approaches.

Environmental Sensors-Based Occupancy Estimation in Buildings via IHMM-MLR

Occupancy estimation in buildings can benefit various applications such as heating, ventilation, and air-conditioning control, space monitoring, and emergency evacuation. Due to the consideration of temporal dependency in occupancy data, hidden Markov model (HMM) has been shown to be effective in occupancy estimation. However, the conventional HMM that assumes invariant temporal dependency of occupancy dynamics for different time instances is unrealistic. Moreover, the performance of the conventional HMM that utilizes mixture of Gaussian for emission probability in terms of continuous observations can be easily affected by the noise in sensory data. To address these problems, in this paper, we propose a new architecture, i.e., inhomogeneous hidden Markov model with multinomial logistic regression (IHMM-MLR), for building occupancy estimation using nonintrusive environmental sensors. Instead of using the time-invariant transition probability matrix, we apply a time-dependent (inhomogeneous) transition probability matrix which can capture the temporal dependency for different time instances. Meanwhile, we employ an efficient probabilistic model, i.e., MLR, for emission probability. Online and offline occupancy estimation schemes are presented for real-time and accurate long-term applications respectively. Real experiments have indicated the effectiveness of our proposed approach.

Constructing accurate and robust HMM/GMM models for an Arabic speech recognition system

Conventional Hidden Markov Model (HMM) based Automatic Speech Recognition (ASR) systems generally utilize cepstral features as acoustic observation and phonemes as basic linguistic units. Some of the most powerful features currently used in ASR systems are Mel-Frequency Cepstral Coefficients (MFCCs). Speech recognition is inherently complicated due to the variability in the speech signal which includes within- and across-speaker variability. This leads to several kinds of mismatch between acoustic features and acoustic models and hence degrades the system performance. The sensitivity of MFCCs to speech signal variability motivates many researchers to investigate the use of a new set of speech feature parameters in order to make the acoustic models more robust to this variability and thus improve the system performance. The combination of diverse acoustic feature sets has great potential to enhance the performance of ASR systems. This paper is a part of ongoing research efforts aspiring to build an accurate Arabic ASR system for teaching and learning purposes. It addresses the integration of complementary features into standard HMMs for the purpose to make them more robust and thus improve their recognition accuracies. The complementary features which have been investigated in this work are voiced formants and Pitch in combination with conventional MFCC features. A series of experimentations under various combination strategies were performed to determine which of these integrated features can significantly improve systems performance. The Cambridge HTK tools were used as a development environment of the system and experimental results showed that the error rate was successfully decreased, the achieved results seem very promising, even without using language models.

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

The adoption of high-accuracy speech recognition algorithms without an effective evaluation of their impact on the target computational resource is impractical for mobile and embedded systems. In this paper, techniques are adopted to minimise the required computational resource for an effective mobile-based speech recognition system. A Dynamic Multi-Layer Perceptron speech recognition technique, capable of running in real time on a state-of-the-art mobile device, has been introduced. Even though a conventional hidden Markov model when applied to the same dataset slightly outperformed our approach, its processing time is much higher. The Dynamic Multi-layer Perceptron presented here has an accuracy level of 96.94% and runs significantly faster than similar techniques.