An accessible introduction to the phonetic analysis of speechcorpora, this workbook-style text provides an extensive set ofexercises to help readers develop the necessary skills to designand carry out experiments in speech research. Offers the first step-by-step treatment of advanced techniquesin experimental phonetics using speech corpora and downloadablesoftware, including the R programming language Introduces methods of analyzing phonetically-labelled speechcorpora, with the goal of testing hypotheses that often arise inexperimental phonetics and laboratory phonology Incorporates an extensive set of exercises and answers toreinforce the techniques introduced Accessibly written with easy-to-follow computer commands andspectrograms of speech Companion website at www.wiley.com/go/harrington,which includes illustrations, video tutorials, appendices, anddownloadable speech corpora for testing purposes.
Discusses techniques in digital speech processing and instructuring and querying annotations from speech corpora Includes substantial coverage of analysis, including measuringgestural synchronization using EMA, the acoustics of vowels,consonant overlap using EPG, spectral analysis of fricatives andobstruents, and the probabilistic classification of acoustic speechdata
Jonathan Harrington is Professor of the Institute of Phonetics and Speech Processing (IPS), University of Munich, Germany. His recent research has primarily focused on modelling the acoustic and perceptual mechanisms of sound change. He is co-editor of Speech Production: Models, Phonetic Processes, and Techniques (with Marija Tabain, 2006) and Techniques in Speech Acoustics (with Steve Cassidy, 1999).
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 F2IF1 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.
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