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.

翻译：自动语音识别（ASR）系统在成人语音数据上的性能已取得显著进展，但由于儿童与成人声音特征存在声学差异，转录儿童语音仍具挑战性。本研究旨在探索将最先进的Conformer-transducer模型适配至儿童语音以提升儿童语音识别性能的潜力。此外，我们将结果与先前在同一数据上微调的自监督wav2vec2模型及半监督多领域Whisper模型进行对比。研究表明，与未微调模型相比，基于儿童语音微调Conformer-transducer模型可显著提升儿童语音的ASR性能。我们还展示了Whisper与wav2vec2在不同儿童语音数据集上的适配效果。详细对比分析表明，在三种方法中，wav2vec2在性能改进上表现出最稳定的一致性。