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<p align="center">LDC2008S02<b><br>
- <a
href="http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2008S02">CSLU:
National Cellular Telephone Speech Release 2.3</a> -<br>
</b></p>
<p align="center">LDC2008T02<b><br>
</b><b>- <a
href="http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2008T02">GALE
Phase 1 Arabic Blog Parallel Text</a> -<br>
</b></p>
<p align="center">LDC2008S03<b><br>
- <a
href="http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2008S03">STC-TIMIT
1.0</a> -<br>
<br>
</b></p>
<p align="center">The Linguistic Data
Consortium (LDC)
would
like to announce the availability of three new publications.<br>
<br>
</p>
<hr size="2" width="100%">
<div align="center"><br>
<b>New Publications</b><br>
</div>
<br>
<p>(1) <a
href="http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2008S02">CSLU:
National Cellular Telephone Speech Release 2.3</a> was created by
the Center for Spoken Language Understanding (CSLU) at OGI School of
Science and Engineering, Oregon Health and Science University,
Beaverton, Oregon. It consists of cellular telephone speech and
corresponding transcripts, specifically, approximately one minute of
speech from 2336 speakers calling from locations throughout the United
States. </p>
<p>Speakers called the CSLU data collection system on cellular
telephones, and they were asked a series of questions. Two prompt
protocols were used: an In Vehicle Protocol for speakers calling from
inside a vehicle and a Not in Vehicle Protocol for those calling from
outside a vehicle. The protocols shared several questions, but each
protocol contained distinct queries designed to probe the conditions of
the caller's in vehicle/not in vehicle surroundings. </p>
<p>The text transcriptions in this corpus were produced using the non
time-aligned word-level conventions described in The CSLU Labeling
Guide, which is included in the documentation for this release. CSLU:
National Cellular Telephone Speech Release 2.3 contains orthographic
and phonetic transcriptions of corresponding speech files. <br>
</p>
<br>
<p align="center"><b>*</b><br>
</p>
<p>(2) <a
href="http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2008T02">GALE
Phase 1 Arabic Blog Parallel Text</a> was prepared by the LDC and
consists of 102K words (222 files) of Arabic blog text and its English
translation from thirty-three sources. This release was used as
training data in Phase 1 of the DARPA-funded GALE program. </p>
<p>The task of preparing this corpus involved four stages of work: data
scouting, data harvesting, formatting, and data selection.</p>
Data scouting involved manually searching the web for suitable blog
text. Data scouts were assigned particular topics and genres along with
a production target in order to focus their web search. Formal
annotation guidelines and a customized annotation toolkit helped data
scouts to manage the search process and to track progress.
<p>Data scouts logged their decisions about potential text of interest
(sites, threads and posts) to a database. A nightly process queried the
annotation database and harvested all designated URLs. Whenever
possible, the entire site was downloaded, not just the individual
thread or post located by the data scout. </p>
<p>Once the text was downloaded, its format was standardized so that
the data could be more easily integrated into downstream annotation
processes. Typically a new script was required for each new domain name
that was identified. After scripts were run, an optional manual process
corrected any remaining formatting problems.</p>
<p>The selected documents were then reviewed for content suitability
using a semi-automatic process. A statistical approach was used to rank
a document's relevance to a set of already-selected documents labeled
as "good." An annotator then reviewed the list of relevance-ranked
documents and selected those which were suitable for a particular
annotation task or for annotation in general. <br>
</p>
<p>After files were selected, they were reformatted into a
human-readable translation format, and the files were then assigned to
professional translators for careful translation. Translators followed
LDC's GALE Translation guidelines, which describe the makeup of the
translation team, the source, data format, the translation data format,
best practices for translating certain linguistic features (such as
names and speech disfluencies), and quality control procedures applied
to completed translations. </p>
<p>All final data are in Tab Delimited Format (TDF). TDF is compatible
with other transcription formats, such as the Transcriber format and AG
format, and it is easy to process. Each line of a TDF file corresponds
to a speech segment and contains 13 tab delimited field.A source TDF
file and its translation are the same except that the transcript in the
source TDF is replaced by its English translation. <br>
</p>
<p align="center"><b>*</b><br>
</p>
<p>(3) <a
href="http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2008S03">STC-TIMIT
1.0</a> is a telephone version of <a
href="http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC93S1">TIMIT
Acoustic Phonetic Continuous Speech Corpus, LDC93S1</a> (TIMIT). TIMIT
contains broadband recordings of 630 speakers of eight major dialects
of American English reading ten phonetically rich sentences. Created in
1993, TIMIT was designed to provide speech data for acoustic-phonetic
studies and for the development and evaluation of automatic speech
recognition systems. In this TIMIT-derived corpus, the entire TIMIT
database was passed
through an actual telephone channel in a single call. Thus, a single
type of channel distortion and noise affect the whole database.</p>
<p>The process was managed using a Dialogic switchboard for the calling
and receiving ends. No transducer (microphone) was employed; the
original digital signal was converted to analog using the switchboard's
A/D converter, transmitted trough a telephone channel and converted
back to digital format before recording. As a result, the only
distortion introduced is that of the telephone channel itself. </p>
<p>The STC-TIMIT 1.0 database is organized in the same manner as in the
original TIMIT corpus: 4620 files belonging to the training partition
and 1680 files belonging to the test partition. Utterances in STC-TIMIT
1.0 are time-aligned with those of TIMIT
with an average precision of 0.125 ms (1 sample), by maximizing the
cross-correlation between pairs of files from each corpus. Thus, labels
from TIMIT may be used for STC-TIMIT 1.0, and the effects of telephone
channels may be studied on a frame-by-frame basis.</p>
<p>Two telephone lines within the same building were connected to a
Dialogic(R) card. One of the lines was used as the calling-end and
played the speech file, while the other line was used as the
receiving-end and recorded the new signal. The whole recording process
was conducted in a single call. <br>
</p>
<p>After recording, the file was pre-cut according to the length of the
corresponding TIMIT database file. Each resulting file was then aligned
to its corresponding file in TIMIT using the xcorr routine in
Matlab(R). Based on these results, the recorded file was sliced again
from the original recorded file using the newly-generated alignments.
Thus, each file in STC-TIMIT 1.0 is aligned to its equivalent in TIMIT
and has the same length. <br>
</p>
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<pre class="moz-signature" cols="72">--
Ilya Ahtaridis
Membership Coordinator
--------------------------------------------------------------------
Linguistic Data Consortium Phone: 1 (215) 573-1275
University of Pennsylvania Fax: 1 (215) 573-2175
3600 Market St., Suite 810 <a class="moz-txt-link-abbreviated" href="mailto:ldc@ldc.upenn.edu">ldc@ldc.upenn.edu</a>
Philadelphia, PA 19104 USA <a class="moz-txt-link-freetext" href="http://www.ldc.upenn.edu">http://www.ldc.upenn.edu</a>
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