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Hands-On Deep Learning for IoT

You're reading from  Hands-On Deep Learning for IoT

Product type Book
Published in Jun 2019
Publisher Packt
ISBN-13 9781789616132
Pages 308 pages
Edition 1st Edition
Languages
Authors (2):
Dr. Mohammad Abdur Razzaque Dr. Mohammad Abdur Razzaque
Profile icon Dr. Mohammad Abdur Razzaque
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
View More author details
Toc

Table of Contents (15) Chapters close

Preface 1. Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
2. The End-to-End Life Cycle of the IoT 3. Deep Learning Architectures for IoT 4. Section 2: Hands-On Deep Learning Application Development for IoT
5. Image Recognition in IoT 6. Audio/Speech/Voice Recognition in IoT 7. Indoor Localization in IoT 8. Physiological and Psychological State Detection in IoT 9. IoT Security 10. Section 3: Advanced Aspects and Analytics in IoT
11. Predictive Maintenance for IoT 12. Deep Learning in Healthcare IoT 13. What's Next - Wrapping Up and Future Directions 14. Other Books You May Enjoy

DL for sound/audio recognition in IoT

It is important to understand the working principle of an Automatic Speech Recognition (ASR) system before discussing the useful DL models.

ASR system model

An Automatic Speech Recognition (ASR) system needs three main sources of knowledge. These sources are known as an acoustic model, a phonetic lexicon, and a language model [4]. Generally, an acoustic model deals with the sounds of language, including the phonemes and extra sounds (such as pauses, breathing, background noise, and so on). On the other hand, a phonetic lexicon model or dictionary includes the words that can be understood by the system, with their possible pronunciations. Finally, a language model includes knowledge about...

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