From CHAT towards ASR: A Hybrid Pipeline for Constructing the HUKILC-CO Hungarian Child Speech Dataset

Developing robust automatic speech recognition (ASR) systems for children remains challenging due to the scarcity of annotated datasets, particularly for underrepresented languages like Hungarian. This paper introduces HUKILC-CO, derived from the original HUKILC dataset, as the first Hungarian child speech corpus tailored for ASR. It isolates child speech from adult dialogue and non-verbal annotations while retaining environmental noise to reflect real-world conditions. We address key challenges, including noisy environments, overlapping speech, and CHAT formatting—via a hybrid alignment pipeline integrating Rev AI’s ASR and Batchalign2’s forced alignment. Our methodology combines fuzzy text matching and temporal buffering to synchronize transcripts with audio, achieving precise utterance- and word-level alignment. The dataset comprises 8.3 hours of high-quality child speech from 62 speakers (4.5–5.5 years), partitioned into speaker-independent training, validation, and test sets to ensure generalization. We further standardize labels by removing non-ASR annotations (e.g., corrections, symbols). HUKILC-CO bridges critical gaps in low-resource ASR research, enabling reproducible benchmarks for child speech technologies. The dataset and alignment pipeline are available for research to support educational and assistive applications.