A New Workflow for Semi-Automatized Annotations: Tests with Long-Form Naturalistic Recordings of Childrens Language Environments
Year: 2017
Type: conference-paper
Abstract: 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-vs-adult-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 cross-database project.
Source: Interspeech (USB)
Institutions: Max Planck Society, Max Planck Institute for Psycholinguistics, Duke University, University of California, Merced, Centre National de la Recherche Scientifique, Université Paris Sciences et Lettres, École Normale Supérieure - PSL, Laboratoire de Sciences Cognitives et Psycholinguistique, École des hautes études en sciences sociales, University of Manitoba, Washington State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2747789846
