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Finally, TIMIT includes demographic data about the speakers, permitting fine-grained study of vocal, social, and gender characteristics.

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.

Two sentences, read by all speakers, were designed to bring out dialect variation: The remaining sentences were chosen to be phonetically rich, involving all phones (sounds) and a comprehensive range of diphones (phone bigrams).

Additionally, the design strikes a balance between multiple speakers saying the same sentence in order to permit comparison across speakers, and having a large range of sentences covered by the corpus to get maximal coverage of diphones.

: Structure of the Published TIMIT Corpus: The CD-ROM contains doc, train, and test directories at the top level; the train and test directories both have 8 sub-directories, one per dialect region; each of these contains further subdirectories, one per speaker; the contents of the directory for female speaker A fourth feature of TIMIT is the hierarchical structure of the corpus.

With 4 files per sentence, and 10 sentences for each of 500 speakers, there are 20,000 files.

The same holds true of text corpora, in the sense that the original text usually has an external source, and is considered to be an immutable artifact.

Any transformations of that artifact which involve human judgment — even something as simple as tokenization — are subject to later revision, thus it is important to retain the source material in a form that is as close to the original as possible.

We can also construct special tabulations (known as paradigms) to illustrate contrasts and systematic variation, as shown in 1.3 for three verbs. At the most abstract level, a text is a representation of a real or fictional speech event, and the time-course of that event carries over into the text itself.

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