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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

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781789139495

Length 312 pages

Edition 1st Edition

Languages

Processing

Tools

NLTK

Concepts

Deep Learning

Authors (5):

Shanmugamani

Arumugam

Byiringiro

Joshi

Muthuswamy

+1 more

View More author details

Table of Contents (20) Chapters

Title Page

Packt Upsell

Foreword

Contributors

Preface

1. Getting Started

2. Text Classification and POS Tagging Using NLTK FREE CHAPTER

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

1. Other Books You May Enjoy

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Index

TTS in deep learning

Over the last few years, the field of TTS has been shaken by several deep learning-based breakthroughs. Here, we will present two of them: WaveNet (probably the most notorious one) and Tacotron (which has the advantage of being an end-to-end approach).

WaveNet, in brief

The WaveNet paper was presented in 2016, and it showed results that outperformed the classical TTS approaches. Basically, WaveNet is an audio generative model. It takes a sequence of audio samples as input and predicts the most likely following audio sample. By adding an extra input, it can be conditioned to accomplish more tasks. For instance, if the audio transcript is additionally provided during training, WaveNet can turn it into a TTS system.

WaveNet uses many interesting ideas to train very deep neural networks. The main concept involves dilated causal convolutions (check out the paper to learn more about them).

In the paper, TTS is tackled, among other tasks, and the model is not directly fed with...

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Authors (5)

Shanmugamani

Shanmugamani

Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of TechnologyMadras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

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Arumugam

Arumugam

Rajesh Arumugam is an ML developer at SAP, Singapore. Previously, he developed ML solutions for smart city development in areas such as passenger flow analysis in public transit systems and optimization of energy consumption in buildings when working with Centre for Social Innovation at Hitachi Asia, Singapore. He has published papers in conferences and has pending patents in storage and ML. He holds a PhD in computer engineering from Nanyang Technological University, Singapore.

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Byiringiro

Byiringiro

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Joshi

Joshi

See other products by Joshi

Muthuswamy

Muthuswamy

See other products by Muthuswamy

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