Machine Learning Algorithms Based on Hidden Markov Models in Low-Speed Speech Codecs for Assessing Speech Quality

2020 ◽  
Author(s):  
Sergey Nikolaevich Kirillov ◽  
Vladimir Timurovich Dmitriev ◽  
Sergey Olegovich Aleksenko
Keyword(s):  
Machine Learning ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Learning Algorithms ◽  
Speech Quality ◽  
Machine Learning Algorithms ◽  
Low Speed

scholarly journals Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Cédric Beaulac ◽  
Fabrice Larribe
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Hidden Markov Models ◽  
Real Time ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Estimation ◽  
Supervised Machine Learning ◽  
Real Time Estimation ◽  
Mobile Target

We propose to use a supervised machine learning technique to track the location of a mobile agent in real time. Hidden Markov Models are used to build artificial intelligence that estimates the unknown position of a mobile target moving in a defined environment. This narrow artificial intelligence performs two distinct tasks. First, it provides real-time estimation of the mobile agent’s position using the forward algorithm. Second, it uses the Baum–Welch algorithm as a statistical learning tool to gain knowledge of the mobile target. Finally, an experimental environment is proposed, namely, a video game that we use to test our artificial intelligence. We present statistical and graphical results to illustrate the efficiency of our method.

scholarly journals A spark-based parallel distributed posterior decoding algorithm for big data hidden Markov models decoding problem

2021 ◽  
Vol 10 (3) ◽  
pp. 789
Author(s):  
Imad Sassi ◽  
Samir Anter ◽  
Abdelkrim Bekkhoucha
Keyword(s):  
Big Data ◽  
Hidden Markov Models ◽  
Large Scale ◽  
Markov Models ◽  
Hidden Markov ◽  
Machine Learning Algorithms ◽  
Decoding Algorithm ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Mapreduce Paradigm

<span lang="EN-US">Hidden </span><span lang="IN">M</span><span lang="EN-US">arkov models (HMMs) are one of machine learning algorithms which have been widely used and demonstrated their efficiency in many conventional applications. This paper proposes a modified posterior decoding algorithm to solve hidden Markov models decoding problem based on MapReduce paradigm and spark’s resilient distributed dataset (RDDs) concept, for large-scale data processing. The objective of this work is to improve the performances of HMM to deal with big data challenges. The proposed algorithm shows a great improvement in reducing time complexity and provides good results in terms of running time, speedup, and parallelization efficiency for a large amount of data, i.e., large states number and large sequences number.</span>

Sign in / Sign up

Export Citation Format

Share Document