
Speech Technology
A Theoretical and Practical Introduction
Michael Hammond(Author)
Cambridge University Press
Will be published approx. on 31. March 2026
Book
Hardback
520 pages
978-1-009-60669-1 (ISBN)
Description
In recent years, speech recognition devices have become central to our everyday lives. Systems such as Siri, Alexa, speech-to-text, and automated telephone services, are built by people applying expertise in sound structure and natural language processing to generate computer programmes that can recognise and understand speech. This exciting new advancement has led to a rapid growth in speech technology courses being added to linguistics programmes; however, there has so far been a lack of material serving the needs of students who have limited or no background in computer science or mathematics. This textbook addresses that need, by providing an accessible introduction to the fundamentals of computer speech synthesis and automatic speech recognition technology, covering both neural and non-neural approaches. It explains the basic concepts in non-technical language, providing step-by-step explanations of each formula, practical activities and ready-made code for students to use, which is also available on an accompanying website.
More details
Language
English
Place of publication
Cambridge
United Kingdom
Illustrations
Worked examples or Exercises
Dimensions
Height: 250 mm
Width: 175 mm
Thickness: 23 mm
Weight
760 gr
ISBN-13
978-1-009-60669-1 (9781009606691)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Book
approx. 03/2026
Cambridge University Press
€47.50
Not yet published
Person
Michael Hammond is Full Professor in the Department of Linguistics at The University of Arizona. His work focuses on phonology, psycholinguistics, and computational linguistics. His notable publications include The Phonology of English (OUP, 1999) and Python for Linguists (CUP, 2020).
Content
1. Overview; 2. Speech; 3. Finite-state language modeling; 4. Statistical language models; 5. Non-neural synthesis; 6. Non-neural recognition; 7. Neural nets; 8. Neural synthesis; 9. Neural recognition; 10. Other technologies.