Deep Learning for NLP and Speech Recognition

Cover of Deep Learning for NLP and Speech Recognition

Book Contents

Within this book, we introduce a thorough survey and exploration of deep learning techniques that have led to state-of-the-art quality on a variety of natural language processing tasks. From text classification, to machine translation, to speech recognition, deep learning is playing a pivotal role. In general, the target audience is graduate students and NLP practitioners, due to the depth of some of the mathematical explanations, however many of the core concepts and case studies can provide insight for those who are less math-savvy.

Speech Recognition

Recent years have given way to more improvements in Speech Recognition than many could ever imagine.

Fig. 8.8: Diagram of statistical speech recognition
Fig. 12.9: End-to-end speech processing network from [KHW17]

Case Studies

Each chapter is accompanied by a case study, showing the application of the techniques introduced in the chapter. In general, the case studies rely on or extend open source libraries that leverage these techniques. Apart from a few exceptions, the majority of the case studies are given in Python. It has become the default language interacting with machine learning libraries due to its presence in academic communities and general popularity. You can find the source code for these case studies in our Github Repository.

Fig. 12.17: Output from the base Deep Speech 2 model. Note how many of the
mistakes seem phonetic and create nonlogical words, such as shee and ashe

Conclusion

The fields of NLP and Speech are currently growing at incredible speeds. We hope that this book serves as a useful resource to understand the foundational elements as well as some of the advanced concepts that have been introduced in recent years.

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Applying AI the right way | Chief Scientist — AI @pachyderm.io | Computer Science @UniOfOxford | Published @SpringerCompSci

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Jimmy Whitaker

Jimmy Whitaker

Applying AI the right way | Chief Scientist — AI @pachyderm.io | Computer Science @UniOfOxford | Published @SpringerCompSci