Book Image

Java for Data Science

By : Richard M. Reese, Jennifer L. Reese
Book Image

Java for Data Science

By: Richard M. Reese, Jennifer L. Reese

Overview of this book

para 1: Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning. Para 2: The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning. Para 3: Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning. para 4: What?s Inside ? Understand data science principles with Java support ? Discover machine learning and deep learning essentials ? Explore data science problems with Java-based solutions
Table of Contents (19 chapters)
Java for Data Science
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Understanding speech recognition


Converting speech to text is an important application feature. This ability is increasingly being used in a wide variety of contexts. Voice input is used to control smart phones, automatically handle input as part of help desk applications, and to assist people with disabilities, to mention a few examples.

Speech consists of an audio stream that is complex. Sounds can be split into phones, which are sound sequences that are similar. Pairs of these phones are called diphones. Utterances consist of words and various types of pauses between them.

The essence of the conversion process involves splitting sounds by silences between utterances. These utterances are then matched to the words that most closely sound like the utterance. However, this can be difficult due to many factors. For example, these differences may be in the form of variances in how words are pronounced due to the context of the word, regional dialects, the quality of the sound, and other factors...