2 citations · 2 across the 1 of their papers we have counts for
3 papers
eess.AS2022
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian
P. Mihajlik, A. Balog, T. E. Gráczi +3
Hungarian is spoken by 15 million people, still, easily accessible Automatic Speech Recognition (ASR) benchmark datasets - especially for spontaneous speech - have been practically…
eess.AS2020
Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR
Balázs Tarján, György Szaszák, Tibor Fegyó +1
Recently Deep Transformer models have proven to be particularly powerful in language modeling tasks for ASR. Their high complexity, however, makes them very difficult to apply in t…
eess.AS2020★ 2 cited
On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech
Balázs Tarján, György Szaszák, Tibor Fegyó +1
Advanced neural network models have penetrated Automatic Speech Recognition (ASR) in recent years, however, in language modeling many systems still rely on traditional Back-off N-g…