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Second DIHARD Challenge Development - SEEDLingS
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Abstract:
*Introduction* Second DIHARD Challenge Development - SEEDLinGS was developed by Duke University and LDC and contains approximately two hours of English child language recordings along with corresponding annotations used in support of the Second DIHARD Challenge. This release, when combined with Second DIHARD Challenge Development - Eleven Sources (LDC2021S10), contains the development set audio data and annotation, except for CHiME-5 audio files, which must be obtained from the University of Sheffield. The DIHARD Challenges are a set of shared tasks on diarization focusing on "hard" diarization; that is, speech diarization for challenging corpora where there was an expectation that existing state-of-the-art systems would fare poorly. As with the first challenge, the second development and evaluation sets were drawn from a diverse sampling of sources including monologues, map task dialogues, broadcast interviews, sociolinguistic interviews, meeting speech, speech in restaurants, clinical recordings, extended child language acquisition recordings, and YouTube videos. *Data* Source data is from the SEEDLingS (The Study of Environmental Effects on Developing Linguistic Skills) corpus, designed to investigate how infants' early linguistic and environmental input plays a role in their learning. Recordings were generated in the home environment of infants in the Rochester, New York area. A subset of that data was annotated by LDC for use in the First and Second DIHARD Challenges. The data in this release consists of files provided in the Second DIHARD Challenge as well as subsequently updated annotated files not provided to second challenge participants. All audio is provided in the form of 16 kHz, 16-bit, mono-channel FLAC files. The diarization for each recording is stored as a NIST Rich Transcription Time Marked (RTTM) file. RTTM files are space-separated text files containing one turn per line. Segmentation files are stored as HTK label files. Each of these files contains one speech segment per line. Scoring regions for each recording are specific by un-partitioned evaluation map (UEM) files. All annotation file types are encoded as UTF-8. More information about the file formats and data sources and domains are in the included documentation. *Updates* None at this time.
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URL: https://catalog.ldc.upenn.edu/LDC2021S11
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Digital Speech Analysis in Progressive Supranuclear Palsy and Corticobasal Syndromes
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In: J Alzheimers Dis (2021)
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| 12 |
The Future of Computational Linguistics: On Beyond Alchemy
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In: Front Artif Intell (2021)
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| 13 |
Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders
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In: NPJ Schizophr (2021)
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| 14 |
Automated analysis of lexical features in Frontotemporal Degeneration
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In: Cortex (2021)
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| 15 |
Automated Analysis of Digitized Letter Fluency Data
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In: Front Psychol (2021)
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| 16 |
Tonal Complexes and Tonal Alignment
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In: North East Linguistics Society (2021)
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Probing Acoustic Representations for Phonetic Properties ...
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