Sign language transcription at the level of meaning components

The Berkeley Transcription System (BTS)

Dan I. Slobin | Psychology, University of California, Berkeley

Nini Hoiting | Royal Institute for the Deaf “H.D. Guyot”, Haren, Netherlands

Michelle Anthony | Psychology, University of California, Berkeley

Yael Biederman | Education, University of California, Berkeley

Marlon Kuntze | Education, Stanford University

Reyna Lindert | Psychology, University of California, Berkeley

Jennie Pyers | Psychology, University of California, Berkeley

Helen Thumann | Education, University of California, Berkeley

Amy Weinberg | Education, University of California, Berkeley

The Berkeley Transcription System (BTS) has been designed for the transcription of sign language videotapes at the level of meaning components. The system is based on efforts to transcribe adult-child interactions in American Sign Language (ASL) and Sign Language of the Netherlands (SLN). The goal of BTS is to provide a standard means of transcribing signed utterances, meeting the following objectives:

–compatibility with CHAT format and CLAN programs (CHILDES)

–linear representation on a continuous typed line, using only ASCII characters

–representation at the level of meaning components

–full representation of elements of polycomponential verbs

–representation of manual and nonmanual elements

–representation of gaze direction, role shift, visual attention

–representation of gestures and other communicative acts

–notation of characteristics of adult-child interaction (child-directed signing, errors, overlap, self-correction).

Keywords: classifiers, ASL, morphology, sign language, transcription

Published online: 18 October 2002
https://doi.org/10.1075/sll.4.1-2.07slo

Cited by

Cited by other publications

Crasborn, Onno A.

2015. In Research Methods in Sign Language Studies, ► pp. 74 ff.

Kouremenos, Dimitrios, Klimis Ntalianis & Stefanos Kollias

2018. A novel rule based machine translation scheme from Greek to Greek Sign Language: Production of different types of large corpora and Language Models evaluation. Computer Speech & Language 51 ► pp. 110 ff.

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