Book Image

Hands-On Markov Models with Python

By : Ankur Ankan, Abinash Panda
Book Image

Hands-On Markov Models with Python

By: Ankur Ankan, Abinash Panda

Overview of this book

Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone. Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs. In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading. By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects.
Table of Contents (11 chapters)

Speech recognition

In the 1950s, Bell Labs was the pioneer in speech recognition. The early designed systems were limited to a single speaker and had a very limited vocabulary. After around 70 years of work, the current speech-recognition systems are able to work with speech from multiple speakers and can recognize thousands of words in multiple languages. A detailed discussion of all the techniques used is beyond the scope of this book as enough work has been done on each technique to have a book on itself.

But the general workflow for a speech-recognition system is to first capture the audio by converting the physical sound into an electrical signal using a microphone. The electrical signal generated by the microphone is analog and needs to be converted to a digital form for storage and processing, for which analog-to-digital converters are used. Once we have the speech in digital...