Papers by Katalin Mady
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian (2022.lrec-1)
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| Challenge: | Hungarian is spoken by 15 million people, yet, easily accessible Automatic Speech Recognition (ASR) benchmark datasets are practically unavailable. |
| Approach: | They propose to use a subset of the BEA spoken Hungarian database to assess ASR, primarily for conversational AI applications. |
| Outcome: | The proposed framework achieves 45% reduction in recognition error rate compared to classical approach without external language model or additional supervised data. |