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Whose talk is this

Given a talk with the name removed, can a model say who gave it? Among the {s.speakers} speakers with {s.minTalks} or more talks it can, most of the time, and where it fails it fails in pairs: the speakers it confuses are the speakers who sound alike.

Supervised117 speakers7,532 talksConfusion pairs ~/general-conference

What stands out

  • The right speaker 74% of the time, in the top five 91%. Chance is 0.9%. Words and phrases plus a stylometric vector, five-fold cross-validation.
  • Style alone gets 70%. With no content words at all, function words, sentence shape and punctuation identify the speaker almost as well as the full vocabulary does.
  • Wilford G. Edling is the most recognisable (100% of 21 talks); A. Theodore Tuttle the least (15% of 34).
  • Brigham Young and Heber C. Kimball are mistaken for each other most, 16 times, 3% of their talks between them; then Heber J. Grant and Joseph F. Smith.
  • The calling is harder than the speaker. With every speaker held out, the text names the calling 42% of the time; Quorum of the Twelve is the most recognisable calling (49%).
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Model card

Data
7,532 talks of 300 words or more by the 117 speakers with 20 or more.
Features and model
Masked TF-IDF over words and two-word phrases (names, years, magazines and citations removed; 40,000 features) concatenated with the standardised stylometric vector of the style study, scaled down by 0.3; a multinomial logistic regression. The style-only model uses the stylometric vector alone.
Validation
Five stratified folds by talk for speaker attribution (the question is whether a new talk by a known speaker can be attributed). For calling, five folds grouped by speaker so the model cannot recognise the person.
Known failure modes
Speakers of one era share vocabulary and transcription conventions, so part of what the model learns is period, not person; the style-only score benefits from the same. Speakers with few talks are harder, and a long-serving speaker's early and late talks can read as two people.

Methods and data for the corpus as a whole.