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HKUST/MTS: A Very Large Scale Mandarin Telephone Speech Corpus

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Chinese Spoken Language Processing (ISCSLP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4274))

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Abstract

The paper describes the design, collection, transcription and analysis of 200 hours of HKUST Mandarin Telephone Speech Corpus (HKUST/MTS) from over 2100 Mandarin speakers in mainland China under the DARPA EARS framework. The corpus includes speech data, transcriptions and speaker demographic information. The speech data include 1206 ten-minute natural Mandarin conversations between either strangers or friends. Each conversation focuses on a single topic. All calls are recorded over public telephone networks. All calls are manually annotated with standard Chinese characters (GBK) as well as specific mark-ups for spontaneous speech. A file with speaker demographic information is also provided. The corpus is the largest and first of its kind for Mandarin conversational telephone speech, providing abundant and diversified samples for Mandarin speech recognition and other application-dependent tasks, such as topic detection, information retrieval, keyword spotting, speaker recognition, etc. In a 2004 evaluation test by NIST, the corpus is found to improve system performance quite significantly.

HKUST Mandarin Telephone Transcripts, Part 1, Linguistic Data Consortium (LDC) catalog number LDC2005T32 and isbn 1-58563-352-6 http://www.ldc.upenn.edu/

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References

  1. Linguistic Data Consortium (LDC), various corpus resources on http://www.ldc.upenn.edu

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© 2006 Springer-Verlag Berlin Heidelberg

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Liu, Y., Fung, P., Yang, Y., Cieri, C., Huang, S., Graff, D. (2006). HKUST/MTS: A Very Large Scale Mandarin Telephone Speech Corpus . In: Huo, Q., Ma, B., Chng, ES., Li, H. (eds) Chinese Spoken Language Processing. ISCSLP 2006. Lecture Notes in Computer Science(), vol 4274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11939993_73

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  • DOI: https://doi.org/10.1007/11939993_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49665-6

  • Online ISBN: 978-3-540-49666-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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