@inproceedings{heiney-etal-2026-autorpt,
title = "{A}uto{RPT}: A Tool for Bootstrapping Prosodic Annotation",
author = "Heiney, Seth and
Hicks, Thomas and
Little, Sally and
Lourenco, Fernanda and
Retana, Kai and
Stevens, Eliana and
Howell, Jonathan",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.443/",
doi = "10.63317/25i8wpabeno4",
pages = "5640--5648",
abstract = "Automated Rapid Prosody Transcription (AutoRPT) is a tool for bootstrapping manual annotation of prosodic events in either corpora or standalone audio files using the Rapid Prosody Transcription (RPT) scheme. It functions by utilizing two Long-Short Term Memory (LSTM) models, trained on measures of pitch/F0 and intensity. In addition to discrete, slightly over-generated predictions of prominence and boundary, AutoRPT produces continuous predictions between 0 and 1, similar to crowd-sourced RPT annotations averaged over listeners. Marginal predictions above a given threshold are also indicated discretely by question marks, as in the PoLaR Annotation Guidelines. Annotators achieved a statistically significant increase in annotation speed by modifying AutoRPT-generated annotations over creating annotations without assistance. In contrast with older tools such as AuToBI (Rosenberg, 2010), AutoRPT generates more theory-agnostic annotations which can support the work of non-expert annotators, and which we expect will offer greater flexibility in the prosodic annotation of other English language varieties."
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<abstract>Automated Rapid Prosody Transcription (AutoRPT) is a tool for bootstrapping manual annotation of prosodic events in either corpora or standalone audio files using the Rapid Prosody Transcription (RPT) scheme. It functions by utilizing two Long-Short Term Memory (LSTM) models, trained on measures of pitch/F0 and intensity. In addition to discrete, slightly over-generated predictions of prominence and boundary, AutoRPT produces continuous predictions between 0 and 1, similar to crowd-sourced RPT annotations averaged over listeners. Marginal predictions above a given threshold are also indicated discretely by question marks, as in the PoLaR Annotation Guidelines. Annotators achieved a statistically significant increase in annotation speed by modifying AutoRPT-generated annotations over creating annotations without assistance. In contrast with older tools such as AuToBI (Rosenberg, 2010), AutoRPT generates more theory-agnostic annotations which can support the work of non-expert annotators, and which we expect will offer greater flexibility in the prosodic annotation of other English language varieties.</abstract>
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%0 Conference Proceedings
%T AutoRPT: A Tool for Bootstrapping Prosodic Annotation
%A Heiney, Seth
%A Hicks, Thomas
%A Little, Sally
%A Lourenco, Fernanda
%A Retana, Kai
%A Stevens, Eliana
%A Howell, Jonathan
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F heiney-etal-2026-autorpt
%X Automated Rapid Prosody Transcription (AutoRPT) is a tool for bootstrapping manual annotation of prosodic events in either corpora or standalone audio files using the Rapid Prosody Transcription (RPT) scheme. It functions by utilizing two Long-Short Term Memory (LSTM) models, trained on measures of pitch/F0 and intensity. In addition to discrete, slightly over-generated predictions of prominence and boundary, AutoRPT produces continuous predictions between 0 and 1, similar to crowd-sourced RPT annotations averaged over listeners. Marginal predictions above a given threshold are also indicated discretely by question marks, as in the PoLaR Annotation Guidelines. Annotators achieved a statistically significant increase in annotation speed by modifying AutoRPT-generated annotations over creating annotations without assistance. In contrast with older tools such as AuToBI (Rosenberg, 2010), AutoRPT generates more theory-agnostic annotations which can support the work of non-expert annotators, and which we expect will offer greater flexibility in the prosodic annotation of other English language varieties.
%R 10.63317/25i8wpabeno4
%U https://aclanthology.org/2026.lrec-1.443/
%U https://doi.org/10.63317/25i8wpabeno4
%P 5640-5648
Markdown (Informal)
[AutoRPT: A Tool for Bootstrapping Prosodic Annotation](https://aclanthology.org/2026.lrec-1.443/) (Heiney et al., LREC 2026)
ACL
- Seth Heiney, Thomas Hicks, Sally Little, Fernanda Lourenco, Kai Retana, Eliana Stevens, and Jonathan Howell. 2026. AutoRPT: A Tool for Bootstrapping Prosodic Annotation. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 5640–5648, Palma de Mallorca, Spain. ELRA Language Resource Association.