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Despite its complexity, the TIMIT corpus only contains two fundamental data types, namely lexicons and texts.
As we saw in 2., most lexical resources can be represented using a record structure, i.e. A lexical resource could be a conventional dictionary or comparative wordlist, as illustrated.
It may come with annotations such as part-of-speech tags, morphological analysis, discourse structure, and so forth.
As we saw in the IOB tagging technique (7.), it is possible to represent higher-level constituents using tags on individual words.
TIMIT was developed by a consortium including Texas Instruments and MIT, from which it derives its name.
It was designed to provide data for the acquisition of acoustic-phonetic knowledge and to support the development and evaluation of automatic speech recognition systems.
As in other chapters, there will be many examples drawn from practical experience managing linguistic data, including data that has been collected in the course of linguistic fieldwork, laboratory work, and web crawling.
The TIMIT corpus of read speech was the first annotated speech database to be widely distributed, and it has an especially clear organization.
TIMIT illustrates several key features of corpus design.
This last observation is less surprising when we consider that text and record structures are the primary domains for the two subfields of computer science that focus on data management, namely text retrieval and databases.
A notable feature of linguistic data management is that usually brings both data types together, and that it can draw on results and techniques from both fields.
Structured collections of annotated linguistic data are essential in most areas of NLP, however, we still face many obstacles in using them.
The goal of this chapter is to answer the following questions: Along the way, we will study the design of existing corpora, the typical workflow for creating a corpus, and the lifecycle of corpus.
The inclusion of speaker demographics brings in many more independent variables, that may help to account for variation in the data, and which facilitate later uses of the corpus for purposes that were not envisaged when the corpus was created, such as sociolinguistics.