@inproceedings{sward-etal-2026-much,
title = "How Much Data for Stable Formant Values? Pipeline for Convergence Detection Based on Read Speech",
author = "Sward, Kayla and
Sjons, Johan and
Ekstrom, Axel G.",
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-1.470/",
doi = "10.63317/3mgdob6szndp",
pages = "5916--5925",
abstract = "This study investigates the stability and convergence of vowel formants (F1, F2, F3) in read speech through an extensive corpus of audiobook recordings. While most formant studies rely on brief, isolated utterances recorded in laboratory settings, this analysis draws on 3,384 chapters (about 942 hours) of continuous, stylistically varied speech from publicly available audiobooks. The data was processed using an automated pipeline that comprised transcription, phoneme alignment, and formant extraction. Several statistical techniques {--} First Token Within (FTW), Cumulative Sum (CUSUM), Two-Sample t-Test, Confidence Interval (CI) Shrinkage, Piecewise Linear Fitting (PWLF), and Binary Segmentation (BinSeg) {--} were compared for their effectiveness in identifying stabilization points. Findings indicate that formant means generally stabilize within 60 to 230 vowel tokens per phoneme, dependent on vowel type and speaker gender. Of the methods that were evaluated, CUSUM yielded the most consistent and informative results. The results provide practical guidelines for determining the quantity of non-laboratory speech required to obtain reliable vowel formant averages."
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<abstract>This study investigates the stability and convergence of vowel formants (F1, F2, F3) in read speech through an extensive corpus of audiobook recordings. While most formant studies rely on brief, isolated utterances recorded in laboratory settings, this analysis draws on 3,384 chapters (about 942 hours) of continuous, stylistically varied speech from publicly available audiobooks. The data was processed using an automated pipeline that comprised transcription, phoneme alignment, and formant extraction. Several statistical techniques – First Token Within (FTW), Cumulative Sum (CUSUM), Two-Sample t-Test, Confidence Interval (CI) Shrinkage, Piecewise Linear Fitting (PWLF), and Binary Segmentation (BinSeg) – were compared for their effectiveness in identifying stabilization points. Findings indicate that formant means generally stabilize within 60 to 230 vowel tokens per phoneme, dependent on vowel type and speaker gender. Of the methods that were evaluated, CUSUM yielded the most consistent and informative results. The results provide practical guidelines for determining the quantity of non-laboratory speech required to obtain reliable vowel formant averages.</abstract>
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%0 Conference Proceedings
%T How Much Data for Stable Formant Values? Pipeline for Convergence Detection Based on Read Speech
%A Sward, Kayla
%A Sjons, Johan
%A Ekstrom, Axel G.
%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 sward-etal-2026-much
%X This study investigates the stability and convergence of vowel formants (F1, F2, F3) in read speech through an extensive corpus of audiobook recordings. While most formant studies rely on brief, isolated utterances recorded in laboratory settings, this analysis draws on 3,384 chapters (about 942 hours) of continuous, stylistically varied speech from publicly available audiobooks. The data was processed using an automated pipeline that comprised transcription, phoneme alignment, and formant extraction. Several statistical techniques – First Token Within (FTW), Cumulative Sum (CUSUM), Two-Sample t-Test, Confidence Interval (CI) Shrinkage, Piecewise Linear Fitting (PWLF), and Binary Segmentation (BinSeg) – were compared for their effectiveness in identifying stabilization points. Findings indicate that formant means generally stabilize within 60 to 230 vowel tokens per phoneme, dependent on vowel type and speaker gender. Of the methods that were evaluated, CUSUM yielded the most consistent and informative results. The results provide practical guidelines for determining the quantity of non-laboratory speech required to obtain reliable vowel formant averages.
%R 10.63317/3mgdob6szndp
%U https://aclanthology.org/2026.lrec-1.470/
%U https://doi.org/10.63317/3mgdob6szndp
%P 5916-5925
Markdown (Informal)
[How Much Data for Stable Formant Values? Pipeline for Convergence Detection Based on Read Speech](https://aclanthology.org/2026.lrec-1.470/) (Sward et al., LREC 2026)
ACL