NDLI: Elicit spoken-style data from social media through a style classifier

Content Provider	IEEE Xplore Digital Library
Author	Chotimongkol, A. Chunwijitra, V. Thatphithakkul, S. Kurpukdee, N. Wutiwiwatchai, C.
Copyright Year	2015
Description	Author affiliation: NECTEC, Nat. Sci. & Technol. Dev. Agency, Pathumthani, Thailand (Chotimongkol, A.; Chunwijitra, V.; Thatphithakkul, S.; Kurpukdee, N.; Wutiwiwatchai, C.)
Abstract	We explore the use of social media data to reduce the effort in developing a conversational speech corpus. The LOTUS-SOC corpus is created by recording Twitter messages through a mobile application. In the first phase, which took around one month, 172 hours of speech from 208 speakers were recorded and ready for use without the need for speech segmentation and transcription. In terms of language similarity to spoken language, the perplexity of LOTUS-SOC with respect to known spoken utterances is lower than that of the broadcast news corpus and almost as low as the telephone conversation corpus. We also applied a style classifier trained from words and parts-of-speech using two machine learning approaches, SVM and CRF, to identify spoken-style utterances in LOTUS-SOC. By training a language model from only the utterances classified as “spoken”, the perplexity of LOTUS-SOC was further reduced as evaluated by three different sets of spoken utterances.
Starting Page	7
Ending Page	12
File Size	226516
Page Count	6
File Format	PDF
e-ISBN	9781467382793
DOI	10.1109/ICSDA.2015.7357856
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training conversational speech corpus Vocabulary Media Data collection language modeling Speech Twitter text classification Speech processing social media LVCSR
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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