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

3. manual phonetic transcription (this reflects the sounds
actually pronounced by the speakers);
4. rule-based phonetic transcription (this level is automatically
generated by a text transcriber);
5. morphemes;
6. orthographic words;
7. prosodic units and pauses;
8. utterances.
There could be two types of manual phonetic transcription:
based on acoustic analysis and based on perceptual analysis.
Our experience shows that these two differ from each
other. Orthographic and prosodic transcription labels, phonetic
transcription labels of word-initial allophones, and
fundamental frequency labels in case of voiced signal are
automatically aligned with each other.
The other kind of annotation is a set of attributes of segmental
units, i.e. words could be attributed as perceptually
prominent words and also have other attributes showing
part of speech and grammatical information.
There are two interconvertible formats we use for storing
annotation. The first one is text format similar to TextGrids
for Praat. The difference is that every annotation level is
stored in separate text file. The other is XML format, where
the annotation is stored as a hierarchical tree structure in
one XML document (Tananayko et al., 2011).
Clear and unified format of files with annotation data enables
use of external software for data processing and analysis.
Besides, we provide tools for exporting annotation to
TextGrid files for further processing with the help of Praat
(Boersma and Weenink, 2012).
The annotation that could be done automatically we did
by means of automatic procedures. We use manual expert
annotation for two types of transcriptions: phonetic
and prosodic. All the other types of transcription are done
by automatic tools with a small amount of further manual
correction. Experts could make mistakes, thus manual annotation
is automatically validated by the help of automatic
tools. Every manual annotation is processed by specially
Figure 1: Annotation Scheme.
44
designed automatic validators before being added into annotation
scheme. This procedure prevents some human
mistakes and increases overall annotation quality. Validator
mainly check if there are misplaced segmentation marks.
The wrongly named labels are hard to validate automatically.
Sometimes the expert annotator responds to the communicative
function of the speech unit and neglects objective
acoustic information. In this situation a qualified phonetician
acts as a native speaker (listener) and even scrupulous
verification does not help.
Recently we used a ASR system to recognize sounds in the
corpus. The result of automatic identification was different
from expert’s manual transcription in about 20% of cases.
Further analysis showed that in some cases automatic identification
was correct. An annotator was guided by phonological
perception of a sound but not by its actual quality.
2.2. Built-in Tools for Data Processing
There are three types of built-in tools within the framework.
First, tools for automatic data processing, feature extraction
and analysis. Second, tools for intelligent data search and
extraction. Third, tools for automatic segmentation and addition
of new information to annotation.
2.2.1. Tools for automatic data processing and
analysis
There is a set of command line tools written in Perl for annotation
data processing and analysis tasks including:
• extraction of the essential segmental unit features, e.g.
duration, amplitude, melody, and timing;
• statistical calculations concerning duration, amplitude
and timing of segmental unit parameters;
• statistical calculations concerning realization of various
segmental units and paralinguistic events within a
given context;
• comparative analysis of various speakers, styles of
speech and discourse, various contexts and collocations;