Phonetic Analysis of Speech Corpora
Phonetic Analysis of Speech Corpora
ISBN: 978-1-405-19957-5 April 2010 Wiley-Blackwell 424 Pages
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An accessible introduction to the phonetic analysis of speech corpora, this workbook-style text provides an extensive set of exercises to help readers develop the necessary skills to design and carry out experiments in speech research.
- Offers the first step-by-step treatment of advanced techniques in experimental phonetics using speech corpora and downloadable software, including the R programming language
- Introduces methods of analyzing phonetically-labelled speech corpora, with the goal of testing hypotheses that often arise in experimental phonetics and laboratory phonology
- Incorporates an extensive set of exercises and answers to reinforce the techniques introduced
- Accessibly written with easy-to-follow computer commands and spectrograms of speech
- Companion website at www.wiley.com/go/harrington, which includes illustrations, video tutorials, appendices, and downloadable speech corpora for testing purposes.
- Discusses techniques in digital speech processing and in structuring and querying annotations from speech corpora
- Includes substantial coverage of analysis, including measuring gestural synchronization using EMA, the acoustics of vowels, consonant overlap using EPG, spectral analysis of fricatives and obstruents, and the probabilistic classification of acoustic speech data
Table of contents
Relationship between Machine Readable (MRPA) and International Phonetic Alphabet (IPA) for Australian English. Relationship between Machine Readable (MRPA) and International Phonetic Alphabet (IPA) for German. Downloadable Speech Databases Used in this Book. Preface. Notes on Downloading Software. 1. Using Speech Corpora in Phonetics Research. 1.1 The Place of Corpora in the Phonetic Analysis of Speech. 1.2 Existing Speech Corpora for Phonetic Analysis. 1.3 Designing Your Own Corpus. 1.4 Summary and Structure of the Book. 2. Some Tools for Building and Querying Annotated Speech Databases. 2.1 Overview. 2.2 Getting Started with Existing Speech Databases. 2.3 Interface between Praat and Emu. 2.4 Interface to R. 2.5 Creating a New Speech Database: From Praat to Emu to R. 2.6 A First Look at the Template File. 2.7 Summary. 2.8 Questions. 3. Applying Routines for Speech Signal Processing. 3.1 Introduction. 3.2 Calculating, Displaying, and Correcting Formants. 3.3 Reading the Formants into R. 3.4 Summary. 3.5 Questions. 3.6 Answers. 4. Querying Annotation Structures. 4.1 The Emu Query Tool, Segment Tiers, and Event Tiers. 4.2 Extending the Range of Queries: Annotations from the Same Tier. 4.3 Inter-tier Links and Queries. 4.4 Entering Structured Annotations with Emu. 4.5 Conversion of a Structured Annotation to a Praat TextGrid. 4.6 Graphical User Interface to the Emu Query Language. 4.7 Re-querying Segment Lists. 4.8 Building Annotation Structures Semi-automatically with Emu-Tcl. 4.9 Branching Paths. 4.10 Summary. 4.11 Questions. 4.12 Answers. 5. An Introduction to Speech Data Analysis in R: A Study of an
EMA Database. 5.1 EMA Recordings and the ema5 Database. 5.2 Handling Segment Lists and Vectors in Emu-R. 5.3 An Analysis of Voice-Onset Time. 5.4 Intergestural Coordination and Ensemble Plots. 5.5 Intragestural Analysis. 5.6 Summary. 5.7 Questions. 5.8 Answers. 6. Analysis of Formants and Formant Transitions. 6.1 Vowel Ellipses in the F2ÍF1 Plane. 6.2 Outliers. 6.3 Vowel Targets. 6.4 Vowel Normalization. 6.5 Euclidean Distances. 6.6 Vowel Undershoot and Formant Smoothing. 6.7 F2 Locus, Place of Articulation, and Variability. 6.8 Questions. 6.9 Answers. 7. Electropalatography. 7.1 Palatography and Electropalatography. 7.2 An Overview of Electropalatography in Emu-R. 7.3 EPG Data-Reduced Objects. 7.4 Analysis of EPG Data. 7.5 Summary. 7.6 Questions. 7.7 Answers. 8. Spectral Analysis. 8.1 Background to Spectral Analysis. 8.2 Spectral Average, Sum, Ratio, Difference, Slope. 8.3 Spectral Moments. 8.4 The Discrete Cosine Transformation. 8.5 Questions. 8.6 Answers. 9. Classification. 9.1 Probability and Bayes’ Theorem. 9.2 Classification: Continuous Data. 9.3 Calculating Conditional Probabilities. 9.4 Calculating Posterior Probabilities. 9.5 Two Parameters: The Bivariate Normal Distribution and Ellipses. 9.6 Classification in Two Dimensions. 9.7 Classifications in Higher Dimensional Spaces. 9.8 Classifications in Time. 9.9 Support Vector Machines. 9.10 Summary. 9.11 Questions. 9.12 Answers. References. Index.
"The book undoubtedly succeeds entirely in its goal to provide an accessible and effective practical introduction to using Emu speech database system and Emu-R functions to analyze phonetic data. It is written in a clear and accessible language and the topics are introduced in a coherent and easy to follow manner with the complexity of the material gradually increasing from the beginning towards the end of the book. Even rather complicated concepts are made easy to understand with an exceptional use of analogy and a commendable restraint from going into too many mathematical and technical details...this is a well-written, well-structured, easy-to-follow workbook which boasts an excellent set of practical exercises and demonstrations and covers a wide range of techniques. Overall, those readers who have a basic background in phonetics and statistics and are prepared to work their way carefully through this book will be greatly rewarded with its informativeness and effectiveness." (LINGUIST List, January 2011)
- An introduction to the quantitative analysis of speech signal processing, computer programming, and statistical techniques in speech sciences
- Helps the reader to develop the necessary skills to create, query, and analyze speech
- Taking a workbook approach, provides an extensive set of exercises with answers for solving problems in phonetics and laboratory phonology
- Uses examples that can be recreated using online samples of acoustic and articulatory speech corpora, and the freely available R software
- Uses text instead of equations where possible, creating an introduction to the subject that is accessible for students from a non-technical background