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30th Anniversary Highlight: CSR 

New publications:
AIDA Ukrainian Broadcast and Telephone Speech Audio and Transcripts
LORELEI Swahili Representative Language Pack

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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) and CSR-II (WSJ1) Complete (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, 1992). 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) 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) and CHiME2 Grid (LDC2017S07) each contain over 120 hours of English speech from a noisy living room environment. CHiME3 (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) 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), 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 Data for more information.

New publications:

AIDA Ukrainian Broadcast and Telephone Speech Audio and Transcripts 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  and are also contained in Multi-Language Conversational Telephone Speech 2011 – Slavic Group LDC2016S11.

2023 members can access this corpus through their LDC accounts. Non-members may license this data for $2000.

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LORELEI Swahili Representative Language Pack 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).

2023 members can access this corpus through their LDC accounts. Non-members may license this data for $250.

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