Automatic speech recognition (ASR) for children remains challenging due to developmental variability and the scarcity of high-quality corpora, especially for Mandarin and its dialects. In this paper, we present ChildTalk, a large-scale Chinese child speech corpus designed to address this gap. It contains 112.5 hours of speech from 498 children (aged 2–8) and 500 caregivers, recorded as natural child–caregiver conversations. Unlike prior Mandarin child ASR corpora that mainly release isolated utterances, ChildTalk provides full-length dialogues with complete transcriptions, preserving turn-taking and discourse context. To our knowledge, it is the first publicly available Mandarin child speech corpus with full-length dialogues and systematic coverage of standard Mandarin, eight Mandarin dialect subgroups, and two additional dialects (Southern Min and Jin). We benchmark end-to-end models trained from scratch, large pre-trained ASR models fine-tuned on ChildTalk, omni-modal LLMs in a zero-shot setting, and commercial speech transcription APIs. Fine-tuning on ChildTalk consistently improves both in-domain and cross-domain performance. These results indicate that ChildTalk provides a challenging, broad-coverage testbed for Chinese child ASR, dialect robustness, and dialogue-level modeling. The dataset will be made freely available for all academic purposes.

Automatic speech recognition (ASR) systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0 and HuBERT. However, developing robust ASR models for young children’s speech remains challenging due to differences in pronunciation, tone, and pace compared to adult speech. In this paper, we introduce a new Mandarin speech dataset focused on children aged 3 to 5, addressing the scarcity of resources in this area. The dataset comprises 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation. We provide a comprehensive analysis of speaker demographics, speech duration distribution and geographic coverage. Additionally, we evaluate ASR performance on models trained from scratch, such as Conformer, as well as fine-tuned pre-trained models like HuBERT and Whisper, where fine-tuning demonstrates significant performance improvements. Furthermore, we assess speaker verification (SV) on our dataset, showing that, despite the challenges posed by the unique vocal characteristics of young children, the dataset effectively supports both ASR and SV tasks. This dataset is a valuable contribution to Mandarin child speech research and holds potential for applications in educational technology and child-computer interaction. It will be open-source and freely available for all academic purposes.