Papers › Nkululeko: A Tool For Rapid Speaker Characteristics Detection
Nkululeko: A Tool For Rapid Speaker Characteristics Detection
Felix Burkhardt, Johannes Wagner, Hagen Wierstorf, Florian Eyben, Björn Schuller
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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