  Hungarian is spoken by 15 million people, still, easily accessible Automatic
Speech Recognition (ASR) benchmark datasets - especially for spontaneous speech
- have been practically unavailable. In this paper, we introduce BEA-Base, a
subset of the BEA spoken Hungarian database comprising mostly spontaneous
speech of 140 speakers. It is built specifically to assess ASR, primarily for
conversational AI applications. After defining the speech recognition subsets
and task, several baselines - including classic HMM-DNN hybrid and end-to-end
approaches augmented by cross-language transfer learning - are developed using
open-source toolkits. The best results obtained are based on multilingual
self-supervised pretraining, achieving a 45% recognition error rate reduction
as compared to the classical approach - without the application of an external
language model or additional supervised data. The results show the feasibility
of using BEA-Base for training and evaluation of Hungarian speech recognition
systems.
