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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.

Like the Brown Corpus, which displays a balanced selection of text genres and sources, TIMIT includes a balanced selection of dialects, speakers, and materials.

The remaining three sentences read by each speaker were unique to that speaker (for coverage). You can access its documentation in the usual way, using This gives us a sense of what a speech processing system would have to do in producing or recognizing speech in this particular dialect (New England).

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

These terms are by no means standard, although the concepts we present here are typical for web-application testing.

First, the corpus contains two layers of annotation, at the phonetic and orthographic levels.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.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.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).