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Hands-On Natural Language Processing with Python

You're reading from   Hands-On Natural Language Processing with Python A practical guide to applying deep learning architectures to your NLP applications

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781789139495
Length 312 pages
Edition 1st Edition
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Authors (5):
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Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
Chaitanya Joshi Chaitanya Joshi
Author Profile Icon Chaitanya Joshi
Chaitanya Joshi
Auguste Byiringiro Auguste Byiringiro
Author Profile Icon Auguste Byiringiro
Auguste Byiringiro
Rajesh Arumugam Rajesh Arumugam
Author Profile Icon Rajesh Arumugam
Rajesh Arumugam
Karthik Muthuswamy Karthik Muthuswamy
Author Profile Icon Karthik Muthuswamy
Karthik Muthuswamy
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Table of Contents (15) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Text Classification and POS Tagging Using NLTK 3. Deep Learning and TensorFlow 4. Semantic Embedding Using Shallow Models 5. Text Classification Using LSTM 6. Searching and DeDuplicating Using CNNs 7. Named Entity Recognition Using Character LSTM 8. Text Generation and Summarization Using GRUs 9. Question-Answering and Chatbots Using Memory Networks 10. Machine Translation Using the Attention-Based Model 11. Speech Recognition Using DeepSpeech 12. Text-to-Speech Using Tacotron 13. Deploying Trained Models 14. Other Books You May Enjoy

Speech Recognition Using DeepSpeech

Speech recognition is the task in which a machine or computer transforms spoken language into text. A good example is the voice typing feature in Google Docs which converts speech to text as you speak. In this chapter, we will look into how to build such systems using deep learning models. In particular, we will focus on using recurrent neural network (RNNs) models as these are found to be effective in practice for speech recognition. This is because RNNs can capture temporal dependencies in the speech data that is important in the task of converting it into text.

The following is an overview of the topics that will be covered in this chapter:

  • Overview of speech recognition
  • RNN models for isolated spoken word recognition
  • Speech recognition using the DeepSpeech model for continuous speech
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