Validating model


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.It could also be a phrasal lexicon, where the key field is a phrase rather than a single word.A thesaurus also consists of record-structured data, where we look up entries via non-key fields that correspond to topics.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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The TIMIT corpus of read speech was the first annotated speech database to be widely distributed, and it has an especially clear organization.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.The inclusion of speaker demographics brings in many more independent variables, that may help to account for variation in the data, and which facilitate later uses of the corpus for purposes that were not envisaged when the corpus was created, such as sociolinguistics.

A third property is that there is a sharp division between the original linguistic event captured as an audio recording, and the annotations of that event.

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.