A Particle Swarm Optimization for Hidden Markov Model Training

2006 ◽  
Author(s):  
Liping Xue ◽  
Junxun Yin ◽  
Zhen Ji ◽  
Lai Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Model Training

Particle Swarm Optimization For Hidden Markov Model

2015 ◽  
Vol 6 (2) ◽  
pp. 1-15
Author(s):  
Nabil M. Hewahi
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Intelligent Systems ◽  
Hidden Markov ◽  
Particle Swarm ◽  
Structure Design ◽  
Second Phase ◽  
Good Representation ◽  
Swarm Optimization

Hidden Markov Model (HMM) is a very well known method as a statistical model used for intelligent systems applications. Due to its involvement in various applications, it would be very important to have a good representation of HMM for the given problem to achieve good results. In this paper, we propose a theoretical approach that can be followed to obtain the best structure of HMM based on Particle Swarm Optimization (PSO) concepts. Given a set of comprehensive visible and invisible states, we propose a method based on PSO concepts to evolve an optimum HMM structure design. The proposed approach deals with two factors related to HMM, generating new states and updating probability values. The main steps followed in the proposed approach involve three main phases, the first phase is generating randomly a population of HMMs, the second phase is converting the generated HMM to PSO required format and the third phase is the application of PSO to find out the optimum HMM . The importance of the proposed approach over other previous approaches is that other approaches deal only with probability updating.

scholarly journals Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Lokesh Selvaraj ◽  
Balakrishnan Ganesan
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Vector Quantization ◽  
Hidden Markov ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.

scholarly journals Electromyography-based gesture recognition for quadriplegic users using hidden Markov model with improved particle swarm optimization

2019 ◽  
Vol 15 (7) ◽  
pp. 155014771986221
Author(s):  
Xanno K Sigalingging ◽  
Alrezza Pradanta Bagus Budiarsa ◽  
Jenq-Shiou Leu ◽  
Jun-ichi Takada
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Life Quality ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Technology System ◽  
Novel Algorithm

People with quadriplegia cannot move their body and limbs freely, making them unable to interact normally with their environment. This article aims to improve the life quality of quadriplegia patients through a development of a system to help them interact with their surroundings. A novel algorithm to classify human gestures is proposed in this article. The algorithm is developed as the core of an assistive technology system in the form of a human interface device, which utilizes electromyograph as its sensor. The system utilizes a wearable electromyograph with a custom software as the signal capturing and processing tool. The electrodes of the electromyograph are placed on certain positions on the face, corresponding to the locations of the major muscles that govern certain facial gestures. The signals are then processed using a novel algorithm that employs hidden Markov model and improved particle swarm optimization to classify the gesture. Based on the gestures, a custom command can be assigned for different conditions. The accuracy of the system is 96.25% for five gestures classification.

Sign in / Sign up

Export Citation Format

Share Document