The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Analyzing linguistic data: a practical introduction to statistics using R

Syllable theory in prosodic phonology

The CLARIN infrastructure aims at providing a technical infrastructure for language resources. In this context we present the design of a web service and a corresponding interface to provide easy access to an formerly only locally executable application for automatic segmentation and labeling called Munich AUtomatic Segmentation (MAUS). Using a standard description format we not only make the manual usage of those services possible, but also enable access from within other services or chaining engines like WebLicht. As an example for the integration of the web service in another application, we show the WebMAUS integration into ELAN.

Signal processing via web services: The use case WebMAUS

Emu-sdms

http://www.lel.ed.ac.uk/~jkirby/docs/roettger2014assessing-preprint.pdf

Assessing incomplete neutralization of final devoicing in German

https://www.phonetik.uni-muenchen.de/personen/mitarbeiter/kleber_felicitas/publikationen/kleberetal10_jphon38.pdf

The implications for speech perception of incomplete neutralization of final devoicing in German

The Emu Speech Database system is a set of software tools developed to support research in acoustic phonetics and other corpus based speech research. This paper describes recent updates that have been made to the latest release of the software.

http://www.phonetik.uni-muenchen.de/~jmh/research/papers/emusst06.pdf

Recent developments in the Emu Speech Database system

Gegenstand dieser Dissertation ist die Weiterentwicklung des bereits bestehenden EMU Speech Database Systems, das Forscher und Studierende der Phonetik und Linguistik beim Aufbau, der Etikettierung, der Abfrage sowie der Analyse von Sprachdatenbanken unterstutzt. EMU kann akustische als auch artikulatorische Daten verarbeiten und sticht besonders durch die umfangreichen Strukturierungsmoglichkeiten fur die Etikettierung, der darauf angepassten Abfragesprache, der Schnittstellte zu R und durch die zentrale Verwaltung der Daten gegenuber anderer vergleichbarer Software hervor.
In Hinsicht auf die Benutzerfreundlichkeit wurden ganzlich fehlende oder luckenhafte Dokumentationen des Systems fur interne als auch externe Strukturen erstellt. Fur bereits vorhandene Funktionen, die nur textbasiert verwendet werden konnten, wurden graﬁsche Benutzerschnittstellen implementiert und die Verwendung somit vereinfacht. Im Rahmen der Funktionalitatserweiterung wurden u. a. die Scripting-Moglichkeiten des Systems auf verschiedene Weisen erweitert und grundsatzlich neue Funktionen hinzugefugt. Die Erweiterung der Interoperabilitat wurde durch eine Funktionalitat zum Datenbankenimport und -export uber indirekte und direkte Schnittstellen zu den Programmen Praat, WaveSurfer sowie Articulate Assistant realisiert. Weiterhin wurde das Kiel Corpus in eine EMU-Sprachdatenbank uberfuhrt. In dieser Form ist es nunmehr in einer hierarchischen Etikettierungsstruktur sehr gut darstellbar, benutzerfreundlich abfragbar und damit einhergehend analysierbar.
Fur die weitere Entwicklung des Systems wird die Uberfuhrung des EMU-Datenmodells in ein relationales vorgeschlagen, da bei geeigneter Modellierung nachweislich die Abfrage auf grose Datenkorpora zeitlich eﬃzienter wird und die Abfragemoglichkeiten erweitert werden.
Die Weiterentwicklungen haben die Software fur die Lehre und Forschung effizient einsetzbar gemacht. EMU kann damit fur sehr viel mehr Anwender einen wichtigen Arbeitsschritt innerhalb des eigenen Arbeitsprozesses zur Losung von Forschungsfragen darstellen.

EMU Speech Database System

The aim of the present experiment was to investigate the acoustic phonetic cues that could underlie a post-stress voicing distinction which, when considered on a segmental basis, appears to be neutralised. The data concern the difference in German between minimal pairs such as 'Enten' and 'enden' which in more casual speaking styles appear to show schwa and oral stop deletion and a surface realisation as a neutralised creaky voice nasal. We extracted from the Kiel Corpus all such contrasts that were judged by trained transcribers to have been neutralised in this way. We measured the spectral slope over the first two harmonics and the time at which the spectral slope first changed significantly. Our results show that, contrary to segmentallybased assumptions, /t, d/ were distinguished depending on the onset time of creaky voice relative to the preceding vowel. These data are consistent with a model in which cues to segmental contrasts may be distributed non-segmentally in time.

/pdf/temporal-alignment-of-creaky-voice-in-neutralised-3vgbyahqu2.pdf

Temporal alignment of creaky voice in neutralised realisations of an underlying, post-nasal voicing contrast in German

In this paper, we demonstrate the advantages of combining the largely complementary systems of Praat, a computational system for doing phonetics, with the EMU system for speech database analysis. The interface applies to the annotations in which a Praat TextGrid is converted into an EMU hierarchical annotation structure and viceversa. With the exception of annotations in EMU that are not explicitly linked to times, we show that there is no loss of information in this conversion. The interface between the Praat and EMU systems provides a flexible labelling system: the data can be labelled as segments or events in Praat and various kinds of structures between annotation tiers can be defined and then queried within EMU. We argue that both the variety of existing speech databases as well as the multitude of different possible types of speech analysis require a modular approach allowing the integration of a number of different stand-alone components that are adapted to different aspects of creating, annotation, querying and analysing speech data.

Tina John

Papers

The implications for speech perception of incomplete neutralization of final devoicing in German

Recent developments in the Emu Speech Database system

EMU Speech Database System

Temporal alignment of creaky voice in neutralised realisations of an underlying, post-nasal voicing contrast in German

Building an interface between EMU and Praat: a modular approach to speech database analysis