Using Coupled Hidden Markov Models to Model Suspect Interactions in Digital Forensic Analysis

2006 ◽  
Author(s):  
Nathan Brewer ◽  
Nianjun Liu ◽  
Olivier De Vel ◽  
Terry Caelli
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Forensic Analysis ◽  
Digital Forensic ◽  
Coupled Hidden Markov Models

Facial event mining using coupled hidden markov models

2005 ◽  
Author(s):  
Limin Ma ◽  
Qiang Zhou ◽  
M. Celenk ◽  
D. Chelberg
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Event Mining ◽  
Coupled Hidden Markov Models

Audio-visual speech asynchrony detection using co-inertia analysis and coupled hidden markov models

2008 ◽  
Vol 12 (3) ◽  
pp. 271-284 ◽  
Author(s):  
Enrique Argones Rúa ◽  
Hervé Bredin ◽  
Carmen García Mateo ◽  
Gérard Chollet ◽  
Daniel González Jiménez
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Visual Speech ◽  
Coupled Hidden Markov Models ◽  
Asynchrony Detection

scholarly journals Variational Inference for Coupled Hidden Markov Models Applied to the Joint Detection of Copy Number Variations

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Xiaoqiang Wang ◽  
Emilie Lebarbier ◽  
Julie Aubert ◽  
Stéphane Robin
Keyword(s):  
Hidden Markov Models ◽  
Copy Number ◽  
Markov Models ◽  
Hidden Markov ◽  
R Package ◽  
Copy Number Variations ◽  
Structural Variations ◽  
Statistical Framework ◽  
Coupled Hidden Markov Models ◽  
Hidden Markov Process

Abstract Hidden Markov models provide a natural statistical framework for the detection of the copy number variations (CNV) in genomics. In this context, we define a hidden Markov process that underlies all individuals jointly in order to detect and to classify genomics regions in different states (typically, deletion, normal or amplification). Structural variations from different individuals may be dependent. It is the case in agronomy where varietal selection program exists and species share a common phylogenetic past. We propose to take into account these dependencies inthe HMM model. When dealing with a large number of series, maximum likelihood inference (performed classically using the EM algorithm) becomes intractable. We thus propose an approximate inference algorithm based on a variational approach (VEM), implemented in the CHMM R package. A simulation study is performed to assess the performance of the proposed method and an application to the detection of structural variations in plant genomes is presented.

Characteristics of the use of coupled hidden Markov models for audio-visual polish speech recognition

2012 ◽  
Vol 60 (2) ◽  
pp. 307-316 ◽  
Author(s):  
M. Kubanek ◽  
J. Bobulski ◽  
L. Adrjanowicz
Keyword(s):  
Speech Recognition ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Visual Speech ◽  
Visual Signals ◽  
Visual Speech Recognition ◽  
Coupled Hidden Markov Models ◽  
Visual Characteristic ◽  
Audio Video

Abstract. This paper focuses on combining audio-visual signals for Polish speech recognition in conditions of the highly disturbed audio speech signal. Recognition of audio-visual speech was based on combined hidden Markov models (CHMM). The described methods were developed for a single isolated command, nevertheless their effectiveness indicated that they would also work similarly in continuous audiovisual speech recognition. The problem of a visual speech analysis is very difficult and computationally demanding, mostly because of an extreme amount of data that needs to be processed. Therefore, the method of audio-video speech recognition is used only while the audiospeech signal is exposed to a considerable level of distortion. There are proposed the authors’ own methods of the lip edges detection and a visual characteristic extraction in this paper. Moreover, the method of fusing speech characteristics for an audio-video signal was proposed and tested. A significant increase of recognition effectiveness and processing speed were noted during tests - for properly selected CHMM parameters and an adequate codebook size, besides the use of the appropriate fusion of audio-visual characteristics. The experimental results were very promising and close to those achieved by leading scientists in the field of audio-visual speech recognition.

Audio-visual speaker identification using coupled hidden Markov models

2004 ◽  
Author(s):  
Tieyan Fu ◽  
Xiao Xing Liu ◽  
Lu Hong Liang ◽  
Xiaobo Pi ◽  
A.V. Nefian
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Speaker Identification ◽  
Hidden Markov ◽  
Coupled Hidden Markov Models
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