The TIMIT corpus of read speech was the first annotated speech database to be widely distributed, and it has an especially clear organization.
TIMIT was developed by a consortium including Texas Instruments and MIT, from which it derives its name.
TIMIT illustrates several key features of corpus design.
First, the corpus contains two layers of annotation, at the phonetic and orthographic levels.
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.
Structured collections of annotated linguistic data are essential in most areas of NLP, however, we still face many obstacles in using them.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.Despite its complexity, the TIMIT corpus only contains two fundamental data types, namely lexicons and texts.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.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.