@inproceedings{burkhardt-etal-2022-nkululeko,
title = "Nkululeko: A Tool For Rapid Speaker Characteristics Detection",
author = {Burkhardt, Felix and
Wagner, Johannes and
Wierstorf, Hagen and
Eyben, Florian and
Schuller, Bj{\"o}rn},
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.205",
pages = "1925--1932",
abstract = "We present advancements with a software tool called Nkululeko, that lets users perform (semi-) supervised machine learning experiments in the speaker characteristics domain. It is based on audformat, a format for speech database metadata description. Due to an interface based on configurable templates, it supports best practise and very fast setup of experiments without the need to be proficient in the underlying language: Python. The paper explains the handling of Nkululeko and presents two typical experiments: comparing the expert acoustic features with artificial neural net embeddings for emotion classification and speaker age regression.",
}
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%0 Conference Proceedings
%T Nkululeko: A Tool For Rapid Speaker Characteristics Detection
%A Burkhardt, Felix
%A Wagner, Johannes
%A Wierstorf, Hagen
%A Eyben, Florian
%A Schuller, Björn
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F burkhardt-etal-2022-nkululeko
%X We present advancements with a software tool called Nkululeko, that lets users perform (semi-) supervised machine learning experiments in the speaker characteristics domain. It is based on audformat, a format for speech database metadata description. Due to an interface based on configurable templates, it supports best practise and very fast setup of experiments without the need to be proficient in the underlying language: Python. The paper explains the handling of Nkululeko and presents two typical experiments: comparing the expert acoustic features with artificial neural net embeddings for emotion classification and speaker age regression.
%U https://aclanthology.org/2022.lrec-1.205
%P 1925-1932
Markdown (Informal)
[Nkululeko: A Tool For Rapid Speaker Characteristics Detection](https://aclanthology.org/2022.lrec-1.205) (Burkhardt et al., LREC 2022)
ACL
- Felix Burkhardt, Johannes Wagner, Hagen Wierstorf, Florian Eyben, and Björn Schuller. 2022. Nkululeko: A Tool For Rapid Speaker Characteristics Detection. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1925–1932, Marseille, France. European Language Resources Association.