Hidden Markov Models work similarly. They help us understand sequences of data where there's some hidden pattern or structure we want to uncover.

In this paper, we propose pattern recognition for tennis tactics using ball trajectory data from the motion capture system. The purpose of the study is to adapt machine learning in order to implement ...

By extracting features in time and frequency domains from the tri-axis accelerometer and tri-axis gyroscope signals, we design and implement a hierarchical classification system to detect complex ...

Repository files navigation 🎙️ Speaker Recognition using Hidden Markov Models (HMM) This project implements a speaker recognition system using Hidden Markov Models (HMMs) and MFCC (Mel Frequency ...

The success of many real-world applications demonstrates that hidden Markov models (HMMs) are highly effective in one-dimensional pattern recognition problems such as speech recognition. Research is ...

This system implements a Hidden Markov Model (HMM) based approach for digit recognition. The core component is the Viterbi algorithm, which determines the most likely sequence of hidden states (digits ...

Hidden Markov models (HMMs) originally emerged in the domain of speech recognition. In recent years, they have attracted growing interest in the area of computer vision as well. This book is a ...

ABSTRACT: This research presents a novel way of labelling human activities from the skeleton output computed from RGB-D data from vision-based motion capture systems. The activities are labelled by ...

To this end, we introduce the Hidden-Articulator Markov Model (HAMM), a model which directly integrates articulatory information into speech recognition. The HAMM is an extension of the ...