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Moreover, notice that all of the data types included in the TIMIT corpus fall into the two basic categories of lexicon and text, which we will discuss below.

Even the speaker demographics data is just another instance of the lexicon data type.

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.

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.

It may come with annotations such as part-of-speech tags, morphological analysis, discourse structure, and so forth.

For each of eight dialect regions, 50 male and female speakers having a range of ages and educational backgrounds each read ten carefully chosen sentences.Finally, notice that even though TIMIT is a speech corpus, its transcriptions and associated data are just text, and can be processed using programs just like any other text corpus.Therefore, many of the computational methods described in this book are applicable.These are organized into a tree structure, shown schematically in 1.2.At the top level there is a split between training and testing sets, which gives away its intended use for developing and evaluating statistical models.

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It could also be a phrasal lexicon, where the key field is a phrase rather than a single word.

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