مقایسهٔ روش‌ها

	Augmentative and Alternative Communication Assessment	Sign Language Corpus Analysis
حوزه	Disability Studies	Disability Studies
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	2001	2010
پدیدآور	AAC field (participation model; feature-matching tradition), framed within the WHO ICF	Trevor Johnston (and the signed-language corpus-linguistics tradition)
نوع	Feature-matching clinical assessment pipeline	Corpus construction and analysis pipeline for signed languages
منبع بنیادین	World Health Organization. (2001). International Classification of Functioning, Disability and Health: ICF. Geneva: WHO. ISBN: 9789241545426	Johnston, T. (2010). From archive to corpus: transcription and annotation in the creation of signed language corpora. International Journal of Corpus Linguistics, 15(1), 106-131.
نام‌های دیگر	AAC Assessment, AAC Feature Matching, Communication Participation Assessment, AAC Needs Assessment	Signed Language Corpus Linguistics, ID-Glossed Sign Corpus, Sign Language Annotation Pipeline, Deaf Corpus Linguistics
مرتبط	3	1
خلاصه	Augmentative and alternative communication (AAC) assessment is the structured process for determining how a person with complex communication needs can best communicate when natural speech is insufficient. Rather than testing for a diagnosis, it follows a participation-oriented, feature-matching logic: the clinician profiles the individual's communication abilities and access capacities, identifies the activities and roles the person wants to take part in, and then matches those needs to the features of AAC systems — the symbol set, access method, vocabulary organization, and output. The approach is grounded in the participation model, which frames the goal as enabling participation in valued life activities rather than remediating an impairment, a stance that aligns closely with the World Health Organization's International Classification of Functioning, Disability and Health and its distinction between body functions, activities, and participation in context. By assessing the person, their environment, and their goals together and then matching to system features, AAC assessment aims to find a communication solution that fits the whole person and is then evaluated and adjusted over time.	Sign language corpus analysis is the methodology for building and studying machine-readable, multimedia collections of signed languages, the natural visual-spatial languages of deaf communities. Because signed languages have no widely used written form, a corpus cannot be a body of text; it must be a structured collection of video recordings of deaf signers, layered with time-aligned annotation that makes the language searchable and analyzable. Trevor Johnston's 2010 account of moving from archive to corpus set out the central methodological principles, most notably ID-glossing, in which every instance of a sign is annotated with a single stable identifier linked to a lexical database so that all tokens of the same sign can be found and counted. The pipeline records signing on video, segments and ID-glosses it, links those glosses to a lexicon, aligns multiple annotation tiers to the video timeline, and then supports quantitative, corpus-based analysis of frequency, variation, and grammar. The result turns a fragile collection of recordings into reusable empirical evidence about how a signed language is actually used.