Szczegóły publikacji
Opis bibliograficzny
Heightceleb - an enrichment of voxceleb dataset with speaker height information / Stanisław KACPRZAK, Konrad KOWALCZYK // W: 2024 IEEE Spoken Language Technology workshop (SLT) [Dokument elektroniczny] : 2-5 December 2024, Macao, China : proceedings / IEEE Signal Processing Society. — Wersja do Windows. — Dane tekstowe. — Danvers, MA : IEEE, cop. 2024. — e-ISBN: 979-8-3503-9225-8. — S. 857-862. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 861-862, Abstr. — Publikacja dostępna online od: 2025-01-16
Autorzy (2)
Dane bibliometryczne
| ID BaDAP | 157969 |
|---|---|
| Data dodania do BaDAP | 2025-02-07 |
| Tekst źródłowy | URL |
| DOI | 10.1109/SLT61566.2024.10832224 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract
Prediction of speaker’s height is of interest for voice forensics, surveillance, and automatic speaker profiling. Until now, TIMIT has been the most popular dataset for training and evaluation of the height estimation methods. In this paper, we introduce HeightCeleb, an extension to VoxCeleb, which is the dataset commonly used in speaker recognition tasks. This enrichment consists in adding information about the height of all 1251 speakers from VoxCeleb that has been extracted with an automated method from publicly available sources. Such annotated data will enable the research community to utilize freely available speaker embedding extractors, pre-trained on VoxCeleb, to build more efficient speaker height estimators. In this work, we describe the creation of the HeightCeleb dataset and show that using it enables to achieve state-of-the-art results on the TIMIT test set by using simple statistical regression methods and embeddings obtained with a popular speaker model (without any additional fine-tuning).
