Editing Idiolect (section)

== Detecting idiolect with corpora ==
Idiolect analysis is different for an individual depending on whether the data being analyzed is from a corpus made up entirely from texts or audio files, since written work is more thought out in planning and precise in wording than in spontaneous speech, which is full of informal language and conversation fillers, e.g. "umm..." and "you know". Corpora with large amounts of input data allow for the generation of word frequency and synonym lists, normally through the use of the top ten bigrams created from it.  In such a situation, the context of word usage is considered, particularly when determining the legitimacy of a given bigram.<ref name="IEaGfPSSM">{{Cite journal|doi=10.1109/TASL.2008.2006578|title=Idiolect Extraction and Generation for Personalized Speaking Style Modeling|year=2009|last1=Wu|first1=Chung-Hsien|last2=Lee|first2=Chung-Han|last3=Liang|first3=Chung-Hau|journal=IEEE Transactions on Audio, Speech, and Language Processing|volume=17|pages=127–137|s2cid=788251}}</ref>

Whether a word or phrase is part of an idiolect is determined by the word's location compared with the window's ''head word'', the edge of the window. This window is kept to 7-10 words, with a sample that is being considered as a feature of the idiolect being possibly +5/-5 words away from the "head" word of the window (which is normally in the middle). Data in corpus pertaining to idiolect get sorted into three categories: irrelevant, personal discourse marker(s), and informal vocabulary. Samples at the end of the frame and far from this head word are often deemed superfluous. Superfluous and non-superfluous data are then run through different functions to see if given words or phrases are a part of an individual's idiolect.<ref name="IEaGfPSSM" />