Automatic Speech Recognition (ASR) systems have progressed significantly in
their performance on adult speech data; however, transcribing child speech
remains challenging due to the acoustic differences in the characteristics of
child and adult voices. This work aims to explore the potential of adapting
state-of-the-art Conformer-transducer models to child speech to improve child
speech recognition performance. Furthermore, the results are compared with
those of self-supervised wav2vec2 models and semi-supervised multi-domain
Whisper models that were previously finetuned on the same data. We demonstrate
that finetuning Conformer-transducer models on child speech yields significant
improvements in ASR performance on child speech, compared to the non-finetuned
models. We also show Whisper and wav2vec2 adaptation on different child speech
datasets. Our detailed comparative analysis shows that wav2vec2 provides the
most consistent performance improvements among the three methods studied.

该研究旨在探索将最先进的 Conformer-transducer 模型适应儿童语音以提高儿童语音识别性能，并将结果与之前在相同数据上进行微调的自监督 wav2vec2 模型和半监督多领域 Whisper 模型进行比较。通过详细的对比分析，我们证明了在儿童语音上微调 Conformer-transducer 模型相对于非微调模型能够显著提高自动语音识别性能。我们还展示了不同儿童语音数据集上的 Whisper 和 wav2vec2 适应情况，并明确 wav2vec2 在这三种方法中提供了最一致的性能改进。