A Variable Initialization Approach to the EM Algorithm for Better Estimation of the Parameters of Hidden Markov Model Based Acoustic Modeling of Speech Signals

Huda, Md. Shamsul; Ghosh, Ranadhir; Yearwood, John

doi:10.1007/11790853_33

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…In the sonar system, multiple measurements can be acquired by sensor arrays. Unlike previous studies using HMM [ 14 , 22 , 23 ], here, multiple measurements were exploited not only to determine the reliable initial values using the genetic algorithm (GA) but also to update parameters using the Baum–Welch algorithm; these are described comprehensively in the following section.…”

Section: Problem Descriptionmentioning

confidence: 99%

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

You

Byun

Choo

2022

Sensors

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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.

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Section: Problem Descriptionmentioning

confidence: 99%

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

You

Byun

Choo

2022

Sensors

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“…This approach applies an HMM in order to classify each sentence in document C into a class corresponding to its co-author. The step (see Sub-section 3.2) for learning of HMM parameters {π π π, B, A} is heavily dependent on the initial values of these parameters (Wu, 1983;Xu and Jordan, 1996;Huda et al, 2006). Therefore, a good initial estimation of the HMM parameters can help achieve a higher classification accuracy.…”

Section: Initializationmentioning

confidence: 99%

Unsupervised Multi-Author Document Decomposition Based on Hidden Markov Model

Aldebei

Jia

et al. 2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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This paper proposes an unsupervised approach for segmenting a multiauthor document into authorial components.The key novelty is that we utilize the sequential patterns hidden among document elements when determining their authorships. For this purpose, we adopt Hidden Markov Model (HMM) and construct a sequential probabilistic model to capture the dependencies of sequential sentences and their authorships. An unsupervised learning method is developed to initialize the HMM parameters. Experimental results on benchmark datasets have demonstrated the significant benefit of our idea and our approach has outperformed the state-of-the-arts on all tests. As an example of its applications, the proposed approach is applied for attributing authorship of a document and has also shown promising results.

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“…Huda et al (Huda et al, 2006) proposed different initial guesses and the solution that corresponds to the local maximum with the largest probability is selected. The authors (Aupetit et al, 2007;Fengqin et al, 2008;Xue et al, 2006) used the Particle Swarm Optimization (PSO) algorithm (Kennedy et al, 2001).…”

Section: New Generative and Discriminative Training Algorithmsmentioning

confidence: 99%

Hidden Markov Models in Dynamic System Modelling and Diagnosis

Al-Ani¹

2011

Hidden Markov Models, Theory and Applications

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A Variable Initialization Approach to the EM Algorithm for Better Estimation of the Parameters of Hidden Markov Model Based Acoustic Modeling of Speech Signals

Cited by 6 publications

References 20 publications

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

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

Unsupervised Multi-Author Document Decomposition Based on Hidden Markov Model

Hidden Markov Models in Dynamic System Modelling and Diagnosis

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