In this newsletter: Renew your LDC membership today 30th Anniversary Highlight: CSR

New publications: AIDA Ukrainian Broadcast and Telephone Speech Audio and Transcriptshttps://catalog.ldc.upenn.edu/LDC2023S01 LORELEI Swahili Representative Language Packhttps://catalog.ldc.upenn.edu/LDC2023T01 ________________________________ Renew your LDC membership today The importance of curated resources for language-related education, research, and technology development drives LDC's mission to create them, to accept data contributions from researchers across the globe, and to broadly share such resources through the LDC Catalog. LDC members enjoy no-cost access to new corpora released annually, as well as the ability to license legacy data sets from among our 925+ holdings at reduced fees. Ensure that your data needs continue to be met by renewing your LDC membership or by joining the Consortium today.

Now through March 1, 2023, 2022 members receive a 10% discount on 2023 membership, and new or returning organizations receive a 5% discount. Membership remains the most economical way to access current and past LDC releases. Consult Join LDChttps://www.ldc.upenn.edu/members/join-ldc for more details on membership options and benefits.

30th Anniversary Highlight: CSR The CSR (continuous speech recognition) corpus series was developed in the early 1990s under DARPA's Spoken Language Program to support research on large-vocabulary CSR systems.

CSR-I (WSJ0) Complete (LDC93S6A)https://catalog.ldc.upenn.edu/LDC93S6A and CSR-II (WSJ1) Complete (LDC94S13A)https://catalog.ldc.upenn.edu/LDC94S13A contain speech from a machine-readable corpus of Wall Street Journal news text. They also include spontaneous dictation by journalists of hypothetical news articles as well as transcripts.

The text in CSR-I (WSJ0) was selected to fall within either a 5,000-word subset or a 20,000-word subset. Audio includes speaker-dependent and speaker-independent sections as well as sentences with verbalized and nonverbalized punctuation. (Doddington, 1992https://aclanthology.org/H92-1074.pdf). CSR-II features "Hub and Spoke" test sets that include a 5,000-word subset and a 64,000-word subset. Both data sets were collected using two microphones: a close-talking Sennheiser HMD414 and a second microphone of varying type.

WSJ0 Cambridge Read News (LDC95S24)https://catalog.ldc.upenn.edu/LDC95S24 was developed by Cambridge University and consists of native British English speakers reading CSR WSJ news text, specifically, sentences from the 5,000-word and 64,000-word subsets. All speakers also recorded a common set of 18 adaptation sentences.

The CSR corpora continue to have value for the research community. CSR-I (WSJ0) target utterances were used in the CHiME2 and CHiME3 challenges which focused on distant-microphone automatic speech recognition in real-world environments. CHiME2 WSJ0 (LDC2017S10)https://catalog.ldc.upenn.edu/LDC2017S10 and CHiME2 Grid (LDC2017S07)https://catalog.ldc.upenn.edu/LDC2017S07 each contain over 120 hours of English speech from a noisy living room environment. CHiME3 (LDC2017S24)https://catalog.ldc.upenn.edu/LDC2017S24 consists of 342 hours of English speech and transcripts from noisy environments and 50 hours of noisy environment audio.

CSR-I target utterances were also used in the Distant-Speech Interaction for Robust Home Applications (DIRHA) Project which addressed natural spontaneous speech interaction with distant microphones in a domestic environment. DIRHA English WSJ Audio (LDC2018S01)https://catalog.ldc.upenn.edu/LDC2018S01 is comprised of approximately 85 hours of real and simulated read speech from native American English speakers in an apartment setting with typical domestic background noises and inter/intra-room reverberation effects.

Multi-Channel WSJ Audio (LDC2014S03)https://catalog.ldc.upenn.edu/LDC2014S03, designed to address the challenges of speech recognition in meetings, contains 100 hours of audio from British English speakers reading sentences from WSJ0 Cambridge Read News. There were three recording scenarios: a single stationary speaker, two stationary overlapping speakers, and one single moving speaker.

All CSR corpora and their related data sets are available for licensing by Consortium members and non-members. Visit Obtaining Datahttps://www.ldc.upenn.edu/language-resources/data/obtaining for more information. ________________________________ New publications:

AIDA Ukrainian Broadcast and Telephone Speech Audio and Transcriptshttps://catalog.ldc.upenn.edu/LDC2023S01 and is comprised of approximately 156 hours of Ukrainian conversational telephone speech and broadcast news audio with 1.2 million words of corresponding orthographic transcripts.

The news audio data was taken from 87 recordings broadcast by various Ukrainian sources. The telephone speech was generated from telephone calls by native Ukrainian speakers to acquaintances in their social network. Native Ukrainian speakers manually segmented the data into sentence-level units as part of the transcription process.

The broadcast recordings and transcripts were produced by LDC to support the DARPA AIDA (Active Interpretation of Disparate Alternatives) program which aimed to develop a multi-hypothesis semantic engine to generate explicit alternative interpretations of events, situations, and trends from a variety of unstructured sources. The telephone speech audio recordings were collected by LDC to support the NIST 2011 Language Recognition Evaluation https://www.nist.gov/itl/iad/mig/2011-language-recognition-evaluation and are also contained in Multi-Language Conversational Telephone Speech 2011 - Slavic Group LDC2016S11https://catalog.ldc.upenn.edu/LDC2016S11.

2023 members can access this corpus through their LDC accounts. Non-members may license this data for a fee. * LORELEI Swahili Representative Language Packhttps://catalog.ldc.upenn.edu/LDC2023T01 was developed by LDC and is comprised of approximately 4.3 million words of Swahili monolingual text, 90,000 Swahili words translated from English data, and 545,000 words of found Swahili-English parallel text. Approximately 100,000 words were annotated for named entities and up to 26,000 words were annotated for entity discovery and linking and situation frames (identifying entities, needs and issues). Data was collected from discussion forum, news, reference, social network, and weblogs.

The LORELEI (Low Resource Languages for Emergent Incidents) program was concerned with building human language technology for low resource languages in the context of emergent situations. Representative languages were selected to provide broad typological coverage.

The knowledge base for entity linking annotation is available separately as LORELEI Entity Detection and Linking Knowledge Base (LDC2020T10)https://catalog.ldc.upenn.edu/LDC2020T10.

2023 members can access this corpus through their LDC accounts. Non-members may license this data for a fee.

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