A new workflow for semi-Automatized annotations: Tests with long-form naturalistic recordings of childrens language environments

Published

Conference Paper

Copyright © 2017 ISCA. Interoperable annotation formats are fundamental to the utility, expansion, and sustainability of collective data repositories. In language development research, shared annotation schemes have been critical to facilitating the transition from raw acoustic data to searchable, structured corpora. Current schemes typically require comprehensive and manual annotation of utterance boundaries and orthographic speech content, with an additional, optional range of tags of interest. These schemes have been enormously successful for datasets on the scale of dozens of recording hours but are untenable for long-format recording corpora, which routinely contain hundreds to thousands of audio hours. Long-format corpora would benefit greatly from (semi-)automated analyses, both on the earliest steps of annotation-voice activity detection, utterance segmentation, and speaker diarization-As well as later steps-e.g., classification-based codes such as child-vsadult-directed speech, and speech recognition to produce phonetic/ orthographic representations. We present an annotation workflow specifically designed for long-format corpora which can be tailored by individual researchers and which interfaces with the current dominant scheme for short-format recordings. The workflow allows semi-Automated annotation and analyses at higher linguistic levels. We give one example of how the workflow has been successfully implemented in a large crossdatabase project. keywords Daylong recordings∗Language acquisition∗Annotation∗Speech recognition∗Speaker diarization.

Full Text

Duke Authors

Cited Authors

  • Casillas, M; Bergelson, E; Warlaumont, AS; Cristia, A; Soderstrom, M; VanDam, M; Sloetjes, H

Published Date

  • January 1, 2017

Published In

Volume / Issue

  • 2017-August /

Start / End Page

  • 2098 - 2102

Electronic International Standard Serial Number (EISSN)

  • 1990-9772

International Standard Serial Number (ISSN)

  • 2308-457X

Digital Object Identifier (DOI)

  • 10.21437/Interspeech.2017-1418

Citation Source

  • Scopus