Best Practices for Speech Corpora in Linguistic Research Workshop ...

Using A Global Corpus Data Model for Linguistic and Phonetic Research
Christoph Draxler
BAS Bavarian Archive of Speech Signals
Institute of Phonetics and Speech Processing
Ludwig-Maximilian University Munich, Germany
draxler@phonetik.uni-muenchen.de
Abstract
This paper presents and discusses the global corpus data model in WikiSpeech for linguistic and phonetic data used at the BAS. The data
model is implemented using a relational database system. Two case studies illustrate how the database is used. In the first case study,
audio recordings performed via the web in Scotland are accessed to carry out formant analyses of Scottish English vowels. In the second
case study, the database is used for online perception experiments on regional variation of speech sounds in German. In both cases, the
global corpus data model has shown to an effective means for providing data in the required formats for the tools used in the workflow,
and to allow the use of the same database for very different types of applications.
1. Introduction
The Bavarian Archive for Speech Signals (BAS) has collected
a number of small and large speech databases using
standalone and web-based tools, e.g. Ph@ttSessionz
(Draxler, 2006), VOYS (Dickie et al., 2009), ALC (Schiel
et al., 2010), and others. Although these corpora were
mainly established to satisfy the needs of speech technology
development, they are now increasingly used in phonetic
and linguistic basic research as well as in education.
This is facilitated by the fact that these speech databases are
demographically controlled, well-documented and quite inexpensive
for academic research.
The workflow for the creation of speech corpora consists
of the steps specification, collection or recording,
signal processing, annotation, postprocessing and distribution
or exploitation – each step involves using dedicated
tools, e.g. SpeechRecorder (Draxler and Jänsch,
2004) to record audio, Praat (Boersma, 2001), ELAN
(Sloetjes et al., 2007), EXMARaLDA (Schmidt and
Wörner, 2005), or WebTranscribe (Draxler, 2005) to annotate
it, libassp (libassp.sourceforge.net), sox
(sox.sourceforge.net) or Praat to perform signal
analysis tasks, and Excel, R and others to carry out statistical
computations (Figure 2).
All tools use their own data formats. Some tools do provide
import and export of other formats, but in general there
is some loss of information in going from one tool to the
other (Schmidt et al., 2009). A manual conversion of formats
is time-consuming and error-prone, and very often the
researchers working with the data do not have the programming
expertise to convert the data. Finally, if each tool provides
its own import and export converter, the number of
such data converters increases dramatically with every new
tool.
We have thus chosen a radical approach: we store all data
independent of any application program in a database system
in a global corpus data model in our WikiSpeech system
(Draxler and Jänsch, 2008).
This global data model goes beyond the general annotation
graph framework for annotations proposed by Liberman
and Bird, which provides a formal description of the
data structures underlying both time-aligned and non time-
51
Administrator
Project
Session
Recording-
Script
Section
SpeechRecorder
Technician
Organization
Person
Speaker
Recording
Signal
Annotator
Praat
Annotation-
Project
Annotation-
Session
Annotation
Tier
Segment
Figure 1: Simplified view of the global corpus data model
in WikiSpeech. The dashed lines show which parts of the
data model are relevant to given tools. For example, Praat
handles signal files and knows about annotation tiers and
(time-aligned) segments on these tiers. SpeechRecorder
knows recording projects that contain recording sessions
which bring together speakers and recording scripts; a
script contains sections which in turn are made up of
recording items which result in signal files.
aligned annotations (Bird and Liberman, 2001). In our
global corpus data model, annotations consist of tiers which
in turn contain segments; segments may be time-aligned
with or without duration, or symbolic, i.e. without time
data. Within a tier segments are ordered sequentially, and
between tiers there exist 1:1, 1:n and n:m relationships.
Besides annotations, our global corpus data model also covers
audio and video recordings, technical information on
the recordings, time-based and symbolic annotations on arbitrarily
many annotation tiers, metadata on the speakers
and the database contents and administrative data on the
annotators (Figure 1.).