Hidden Markov Model for Masquerade Detection Based on Sequence Alignment

This paper presents a parallel phoneme recognition algorithm based on the continuous Hidden Markov Model (HMM). The parallel phoneme recognition algorithm distributes 3-state HMMs of context dependent phonemes to the multiprocessors, computes output probabilities in parallel, and enhances the Viterbi beam search with a message passing mechanism. The algorithm is implemented in a multi-transputer system using distributed-memory MIMD multiprocessors. Experimental results show the feasibility of the parallel phoneme recognition algorithm in constructing a real-time parallel speech recognition system based on time-consuming continuous HMM.

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

In spotting for phrases in text images, speed and accuracy are important considerations. In a hidden Markov model (HMM) based spotter recognition time is dominated by the time required to compute the state conditional observation probabilities. These probabilities are a measure of how well the data match each state in the model. In this paper discrete and continuous hidden Markov models are compared based on speed and accuracy in spotting for phrases in text images. For the discrete HMM, vector quantization is used to associate each continuous feature vector with a discrete value. For the continuous HMMs, the observation distributions for the feature vectors are modeled by either a single Gaussian, or a mixture of two Gaussians. Comparisons were made on a subset of the UW English Document Image Database I. The best accuracy was observed when a mixture of two Gaussians was used in the continuous HMM. The discrete HMM provides for faster spotting particularly when long phrases are used.

Multi metric functional connectivity analysis based on continuous hidden Markov model with application in early diagnosis of Alzheimer’s disease

Sequentially spherical data modeling with hidden Markov models and its application to fMRI data analysis

Masquerade detection is the ability to detect attackers known as masqueraders that intrude on another user’s system and pose as legitimate users. Once a masquerader obtains access to a user’s system, the masquerader has free reign over whatever data is on that system. In this research, we focus on masquerade detection and user classi cation using the following two di erent approaches: the heavy hitter approach and 2 di erent approaches based on hidden Markov models (HMMs), the dueling-HMM and threshold-HMM strategies. The heavy hitter approach computes the frequent elements seen in the training data sequence and test data sequence and computes the distance to see whether the test data sequence is masqueraded or not. The results show very misleading classi cations, suggesting that the approach is not viable for masquerade detection. A hidden Markov model is a tool for representing probability distributions over sequences of observations [9]. Previous research has shown that using a threshold-based hidden Markov model (HMM) approach is successful in a variety of categories: malware detection, intrusion detection, pattern recognition, etc. We have veri ed that using a threshold-based HMM approach produces high accuracy with low amounts of a false positives. Using the dueling- HMM approach, which utilizes multiple training HMMs, we obtain an overall accuracy of 81.96%. With the introduction of the bias in the dueling-HMM approach, we produce similar results to the results obtained in the threshold-based HMM approach, where we see many non-masqueraded data detected, while many masqueraded data avoid detection, yet still result in an high overall accuracy.

This paper explores the issue of recognizing, generalizing and reproducing arbitrary gestures. We aim at extracting a representation that encapsulates only the key aspects of the gesture and discards the variability intrinsic to each person's motion. We compare a decomposition into principal components (PCA) and independent components (ICA) as a first step of preprocessing in order to decorrelate and denoise the data, as well as to reduce the dimensionality of the dataset to make this one tractable. In a second stage of processing, we explore the use of a probabilistic encoding through continuous Hidden Markov Models (HMMs), as a way to encapsulate the sequential nature and intrinsic variability of the motions in stochastic finite state automata. Finally, the method is validated in a humanoid robot to reproduce a variety of gestures performed by a human demonstrator.

This paper presents a novel approach to explicit duration modelling for HMM-based speech synthesis. The proposed approach is a two-step process. The first step in this process is state level phone alignment and conversion of phone durations into the number of frames. In the second step, a hidden Markov model (HMM) is trained whereby the observation is the number of frames in each state and the hidden state the phone. Finally, the duration of each state (the number of frames) is generated from the trained HMM. Hidden semi-Markov model (HSMM) is the baseline for explicit duration modelling in HMM-based speech synthesis. Both objective and perceptual evaluation on a held-out test set showed comparable results with a baseline HSMM-based speech synthesis. This duration modelling approach is computationally simpler than HSMM and produces comparable results in terms of the quality of synthetic speech.

In order to make Intelligent Transportation System (ITS) work effectively, a driver intention recognition method is proposed. In this research, three different recognition models were developed based on Continuous Hidden Markov Model (CHMM), and could distinguish left and right lane change intention from normal lane keeping intention. Subjects performed lane change maneuvers and lane keeping maneuvers with driving simulator which simulated highway scenes, parameters that highly correlated with lane change behavior were collected and analyzed. A series of testings and comparisons were done to obtain the optimal model structure and feature set. Results show that, taking the steering wheel angel, steering wheel angle velocity and lateral acceleration as the optimal observation signals, the accuracy can achieve up 95%, and it proved very effective in terms of early intention recognition.

Continuous hidden Markov models integrating transitional and instantaneous features for Mandarin syllable recognition

A possible interaction of (volcano-) tectonic earthquakes with the continuous seismic noise recorded in the volcanic island of Tenerife was recently suggested, but existing catalogues seem to be far from being self consistent, calling for the development of automatic detection and classification algorithms. In this work we propose the adoption of a methodology based on Hidden Markov Models (HMMs), widely used already in other fields, such as speech classification.

In this paper, a new mimesis scheme is proposed. This scheme enables for a humanoid to imitate human's motion even though the humanoid cannot see human's whole-body motion and the humanoid has not seen the exactly same motion so far. Mimesis framework is based on continuous hidden Markov model. Viterbi algorithm is applied in order to generate more various motion patterns than the number of existing hidden Markov models. In order to imitate other's motion in a smooth way, a smoothing technique in generation problem is realized. The feasibility of this method is demonstrated by simulation on a 20 degrees of freedom humanoid robot configuration

In this chapter, a speech recognition system is proposed to recognize spoken words using a continuous hidden Markov model (HMM). Three datasets of human sounds are collected and used to train and test the model. Features are extracted from the sound data and used to categorize each sound into one of a set of classes. The output is a word in the dictionary. Both person-dependent and person-independent speech recognition are explored. A detailed experiment is performed, and successful recognition results are reported. The study highlights how Arabic words containing certain sounds are recognized differently and distinguishes the specific characteristics of hidden Markov models necessary to recognize Arabic as opposed to English words.

A framework based on hidden Markov model with adaptive weighting for microcystin forecasting and early-warning

Due to the rapid advances in algorithms, VLSI design and computer technology, security systems based on speaker recognition are on the verge of commercial success. In this thesis, an improved strategy for Text Tndependent Automatic Speaker Verification (TI-ASV) system based on Malayalam has been proposed and results are observed. The system performs on Gaussian Mixture Model (GMM) technique with cepstral based features. Different speech pre-processing techniques like pre-emphasis filtering, frame blocking and windowing have been used to process the speech utterances. MFCC, ΔMFCC and ΔΔMFCC have been used to extract the features. Speaker identification is performed using Continuous Hidden Markov Model. The performance is analyzed in terms of Percentage Correctness (PC) and accuracy. An application with Graphical User Interface (GUI) is also developed for security purposes using the system. The system is developed using the framework of Hidden Markov Model Tool Kit (HTK).