  Reduplication and repetition, though similar in form, serve distinct
linguistic purposes. Reduplication is a deliberate morphological process used
to express grammatical, semantic, or pragmatic nuances, while repetition is
often unintentional and indicative of disfluency. This paper presents the first
large-scale study of reduplication and repetition in speech using computational
linguistics. We introduce IndicRedRep, a new publicly available dataset
containing Hindi, Telugu, and Marathi text annotated with reduplication and
repetition at the word level. We evaluate transformer-based models for
multi-class reduplication and repetition token classification, utilizing the
Reparandum-Interregnum-Repair structure to distinguish between the two
phenomena. Our models achieve macro F1 scores of up to 85.62% in Hindi, 83.95%
in Telugu, and 84.82% in Marathi for reduplication-repetition classification.
