Comparative study of Markov Model based synthesis and recognition systems

In password guessing, the Markov model is still widely used due to its simple structure and fast inference speed. However, the Markov model based on random sampling to generate passwords has the problem of a high repetition rate, which leads to a low cover rate. The model based on enumeration has a lower cover rate for high-probability passwords, and it is a deterministic algorithm that always generates the same passwords in the same order, making it vulnerable to attack. We design a dynamic distribution mechanism based on the random sampling method. This mechanism enables the probability distribution of passwords to be dynamically adjusted and tend toward uniform distribution strictly during the generation process. We apply the dynamic distribution mechanism to the Markov model and propose a dynamic Markov model. Through comparative experiments on the RockYou dataset, we set the optimal adjustment degree α. Compared with the Markov model without the dynamic distribution mechanism, the dynamic Markov model reduced the repetition rate from 75.88% to 66.50% and increased the cover rate from 37.65% to 43.49%. In addition, the dynamic Markov model had the highest cover rate for high-probability passwords. Finally, the model avoided the lack of a deterministic algorithm, and when it was run five times, it reached almost the same cover rate as OMEN.

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A hidden Markov model based approach to music segmentation and identification

Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint ◽

10.1109/icics.2003.1292732 ◽

2004 ◽

Author(s):

Sheng Gao ◽

N.C. Maddage ◽

Chin-Hui Lee

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Model Based

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Development of Speech Recognition Systems in Emergency Call Centers

Symmetry ◽

10.3390/sym13040634 ◽

2021 ◽

Vol 13 (4) ◽

pp. 634

Author(s):

Alakbar Valizada ◽

Natavan Akhundova ◽

Samir Rustamov

Keyword(s):

Speech Recognition ◽

Markov Model ◽

Hidden Markov ◽

Call Centers ◽

The Other ◽

Language Models ◽

Emergency Call ◽

Acoustic Model ◽

Different Types ◽

Recognition Systems

In this paper, various methodologies of acoustic and language models, as well as labeling methods for automatic speech recognition for spoken dialogues in emergency call centers were investigated and comparatively analyzed. Because of the fact that dialogue speech in call centers has specific context and noisy, emotional environments, available speech recognition systems show poor performance. Therefore, in order to accurately recognize dialogue speeches, the main modules of speech recognition systems—language models and acoustic training methodologies—as well as symmetric data labeling approaches have been investigated and analyzed. To find an effective acoustic model for dialogue data, different types of Gaussian Mixture Model/Hidden Markov Model (GMM/HMM) and Deep Neural Network/Hidden Markov Model (DNN/HMM) methodologies were trained and compared. Additionally, effective language models for dialogue systems were defined based on extrinsic and intrinsic methods. Lastly, our suggested data labeling approaches with spelling correction are compared with common labeling methods resulting in outperforming the other methods with a notable percentage. Based on the results of the experiments, we determined that DNN/HMM for an acoustic model, trigram with Kneser–Ney discounting for a language model and using spelling correction before training data for a labeling method are effective configurations for dialogue speech recognition in emergency call centers. It should be noted that this research was conducted with two different types of datasets collected from emergency calls: the Dialogue dataset (27 h), which encapsulates call agents’ speech, and the Summary dataset (53 h), which contains voiced summaries of those dialogues describing emergency cases. Even though the speech taken from the emergency call center is in the Azerbaijani language, which belongs to the Turkic group of languages, our approaches are not tightly connected to specific language features. Hence, it is anticipated that suggested approaches can be applied to the other languages of the same group.

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Hidden Markov model based localization using array antenna

2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC) ◽

10.1109/pimrc.2012.6362772 ◽

2012 ◽

Cited By ~ 3

Author(s):

Yusuke Inatomi ◽

Jihoon Hong ◽

Tomoaki Ohtsuki

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Array Antenna ◽

Model Based

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An unsupervised Hidden Markov Model-based system for the detection and classification of blue whale vocalizations off Chile

Bioacoustics ◽

10.1080/09524622.2018.1563758 ◽

2019 ◽

Vol 29 (2) ◽

pp. 140-167 ◽

Cited By ~ 1

Author(s):

Susannah J. Buchan ◽

Rodrigo Mahú ◽

Jorge Wuth ◽

Naysa Balcazar-Cabrera ◽

Laura Gutierrez ◽

...

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Blue Whale ◽

Model Based

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A hybrid hidden Markov model towards fault detection of rotating components

Journal of Vibration and Control ◽

10.1177/1077546315627934 ◽

2016 ◽

Vol 23 (19) ◽

pp. 3175-3195 ◽

Cited By ~ 8

Author(s):

Ayan Sadhu ◽

Guru Prakash ◽

Sriram Narasimhan

Keyword(s):

Fault Detection ◽

Markov Model ◽

Hidden Markov Model ◽

Time Scale ◽

Hidden Markov ◽

Energy Operator ◽

Fault Classification ◽

Multiple Faults ◽

Condition Indicators ◽

Model Based

A robust hybrid hidden Markov model-based fault detection method is proposed to perform multi-state fault classification of rotating components. The approach presented in this paper enhances the performance of the standard hidden Markov model (HMM) for fault detection by performing a series of pre-processing steps. First, the de-noised time-scale signatures are extracted using wavelet packet decomposition of the vibration data. Subsequently, the Teager Kaiser energy operator is employed to demodulate the time-scale components of the raw vibration signatures, following which the condition indicators are calculated. Out of several possible condition indicators, only relevant features are selected using a decision tree. This pre-processing improves the sensitivity of condition indicators under multiple faults. A Gaussian mixing model-based hidden Markov model (HMM) is then employed for fault detection. The proposed hybrid HMM is an improvement over traditional HMM in that it achieves better separation of the feature space leading to more robust state estimation under multiple fault states and measurement noise scenarios. A simulation employing modulated signals and two experimental validation studies are presented to demonstrate the performance of the proposed method.

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