A Semi-Automatic Approach to Create Large Gender- and Age-Balanced Speaker Corpora: Usefulness of Speaker Diarization & Identification
<|reference_start|>A Semi-Automatic Approach to Create Large Gender- and Age-Balanced Speaker Corpora: Usefulness of Speaker Diarization & Identification: This paper presents a semi-automatic approach to create a diachronic corpus of voices balanced for speaker's age, gender, and recording period, according to 32 categories (2 genders, 4 age ranges and 4 recording periods). Corpora were selected at French National Institute of Audiovisual (INA) to obtain at least 30 speakers per category (a total of 960 speakers; only 874 have be found yet). For each speaker, speech excerpts were extracted from audiovisual documents using an automatic pipeline consisting of speech detection, background music and overlapped speech removal and speaker diarization, used to present clean speaker segments to human annotators identifying target speakers. This pipeline proved highly effective, cutting down manual processing by a factor of ten. Evaluation of the quality of the automatic processing and of the final output is provided. It shows the automatic processing compare to up-to-date process, and that the output provides high quality speech for most of the selected excerpts. This method shows promise for creating large corpora of known target speakers.<|reference_end|>
@article{uro2024a,
    title={A Semi-Automatic Approach to Create Large Gender- and Age-Balanced
  Speaker Corpora: Usefulness of Speaker Diarization & Identification},
    author={R'emi Uro, David Doukhan, Albert Rilliard, La"etitia Larcher,
  Anissa-Claire Adgharouamane, Marie Tahon, Antoine Laurent},
    journal={Proceedings of the 13th Conference on Language Resources and
  Evaluation (LREC 2022), pages 3271-3280, Marseille, 20-25 June 2022. European
  Language Resources Association (ELRA)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17552},
    primaryClass={eess.AS cs.CL cs.DL cs.LG cs.SD}
}