Enhancement of connected words in an extremely noisy environment

Cohen, Yishai; Erell, A.; Bistritz, Y.

doi:10.1109/89.554776

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…A general heuristic method to solve the underflow problem is to re-scale the forward-backward probabilities by multiplying a large factor whenever an underflow is likely to occur [106,40]. However, the scale factors cannot guarantee the backward variables being bounded or immunizing from the underflow problem, as pointed out by Murphy [126].…”

Section: Methodsmentioning

confidence: 99%

Hidden semi-Markov models

Shu

2010

Artificial Intelligence

598

416

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As an extension to the popular hidden Markov model (HMM), a hidden semi-Markov model (HSMM) allows the underlying stochastic process to be a semi-Markov chain. Each state has variable duration and a number of observations being produced while in the state. This makes it suitable for use in a wider range of applications. Its forwardbackward algorithms can be used to estimate/update the model parameters, determine the predicted, filtered and smoothed probabilities, evaluate goodness of an observation sequence fitting to the model, and find the best state sequence of the underlying stochastic process. Since the HSMM was initially introduced in 1980 for machine recognition of speech, it has been applied in thirty scientific and engineering areas, such as speech recognition/synthesis, human activity recognition/prediction, handwriting recognition, functional MRI brain mapping, and network anomaly detection. There are about three hundred papers published in the literature. An overview of HSMMs is presented in this paper, including modelling, inference, estimation, implementation and applications. It first provides a unified description of various HSMMs and discusses the general issues behind them. The boundary conditions of HSMM are extended. Then the conventional models, including the explicit duration, variable transition, and residential time of HSMM, are discussed. Various duration distributions and observation models are presented. Finally, the paper draws an outline of the applications.

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Section: Methodsmentioning

confidence: 99%

Hidden semi-Markov models

Shu

2010

Artificial Intelligence

598

416

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“…(1) Input: observation , frequency threshold Γ (2) Output: closed frequent string set L (3) # Find out the frequent strings (4) Initialization: frequent candidate set 1 = Σ, = 1 (5) while ̸ = do (6) # Check frequency of strings in (7) for ∈ do (8) # Freq( ) is the frequency of in (9) if Freq( ) < Γ then (10) Delete from (11) end if (12) end for (13) # end if (20) end for (21) = + 1; (22) end while (23) # Find out the closed frequent strings (24) Initialization: = 1 (25) while +1 ̸ = do (26) for 1 ∈ do (27) for 2 ∈ +1 do (28) # delete the substrings (29) if 1 ⊂ 2 then (30) Delete 1 from (31) Break (32) end if (33) end for (34) end for (35) Update L = L ∪ (36) = + 1 (37) end while (38) Update L = L ∪ Algorithm 1: Closed frequent string algorithm.…”

Section: System Overviewmentioning

confidence: 99%

“…Thirdly, the forward-backward probabilities are adjusted by multiplying a scaling factor whenever an underflow is likely to occur [27,34,35]. In this paper, we tackle the underflow problem of HsMM based on this scaling method.…”

Section: Underflow Problemmentioning

confidence: 99%

Analyzing Network Protocols of Application Layer Using Hidden Semi-Markov Model

Cai

Luo

Lei

2016

Mathematical Problems in Engineering

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With the rapid development of Internet, especially the mobile Internet, the new applications or network attacks emerge in a high rate in recent years. More and more traffic becomes unknown due to the lack of protocol specifications about the newly emerging applications. Automatic protocol reverse engineering is a promising solution for understanding this unknown traffic and recovering its protocol specification. One challenge of protocol reverse engineering is to determine the length of protocol keywords and message fields. Existing algorithms are designed to select the longest substrings as protocol keywords, which is an empirical way to decide the length of protocol keywords. In this paper, we propose a novel approach to determine the optimal length of protocol keywords and recover message formats of Internet protocols by maximizing the likelihood probability of message segmentation and keyword selection. A hidden semi-Markov model is presented to model the protocol message format. An affinity propagation mechanism based clustering technique is introduced to determine the message type. The proposed method is applied to identify network traffic and compare the results with existing algorithm.

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Speaker Independent Connected Digit Recognition Using VQ and HMM in Additive Noise Environment

Revathi¹,

Venkataramani²

2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Enhancement of connected words in an extremely noisy environment

Cited by 7 publications

References 12 publications

Hidden semi-Markov models

Hidden semi-Markov models

Analyzing Network Protocols of Application Layer Using Hidden Semi-Markov Model

Speaker Independent Connected Digit Recognition Using VQ and HMM in Additive Noise Environment

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