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Five of the sentences read by each speaker are also read by six other speakers (for comparability).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).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).

In general, a text or speech corpus may be annotated at many different linguistic levels, including morphological, syntactic, and discourse levels.Moreover, even at a given level there may be different labeling schemes or even disagreement amongst annotators, such that we want to represent multiple versions.A second property of TIMIT is its balance across multiple dimensions of variation, for coverage of dialect regions and diphones.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.

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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.

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  1. Introduction Electron spin resonance (ESR) analysis has recently become an alternative C14 and thermo-luminescence dating method which can be applied to a variety of problems in geology, archaeology and paleoanthropology (Renfrew C et al., 1992; Jonas 1997; Walker 2005; Grun 1989).