The RATS Collection: Supporting HLT Research with Degraded Audio Data

17035

Authors

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Abstract

AbstractThe DARPA RATS program was established to foster development of language technology systems that can perform well on speaker-to-speaker communications over radio channels that evince a wide range in the type and extent of signal variability and acoustic degradation. Creating suitable corpora to address this need poses an equally wide range of challenges for the collection, annotation and quality assessment of relevant data. This paper describes the LDC’s multi-year effort to build the RATS data collection, summarizes the content and properties of the resulting corpora, and discusses the novel problems and approaches involved in ensuring that the data would satisfy its intended use, to provide speech recordings and annotations for training and evaluating HLT systems that perform 4 specific tasks on difficult radio channels: Speech Activity Detection (SAD), Language Identification (LID), Speaker Identification (SID) and Keyword Spotting (KWS).

Keywords

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Full Content

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Labeled Text

AbstractThe DARPA RATS program was established to foster development of language technology systems that can perform well on speaker-to-speaker communications over radio channels that evince a wide range in the type and extent of signal variability and acoustic degradation. Creating suitable corpora to address this need poses an equally wide range of challenges for the collection, annotation and quality assessment of relevant data. This paper describes the LDC’s multi-year effort to build the RATS data collection, summarizes the content and properties of the resulting corpora, and discusses the novel problems and approaches involved in ensuring that the data would satisfy its intended use, to provide speech recordings and annotations for training and evaluating HLT systems that perform 4 specific tasks on difficult radio channels: Speech Activity Detection (SAD), Language Identification (LID), Speaker Identification (SID) and Keyword Spotting (KWS).

Publication Details

Paper ID:
17035
Year:
2014
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan