Automated Speech Recognition shows superhuman performance for adult English
speech on a range of benchmarks, but disappoints when fed children's speech.
This has long sat in the way of child-robot interaction. Recent evolutions in
data-driven speech recognition, including the availability of Transformer
architectures and unprecedented volumes of training data, might mean a
breakthrough for child speech recognition and social robot applications aimed
at children. We revisit a study on child speech recognition from 2017 and show
that indeed performance has increased, with newcomer OpenAI Whisper doing
markedly better than leading commercial cloud services. While transcription is
not perfect yet, the best model recognises 60.3% of sentences correctly barring
small grammatical differences, with sub-second transcription time running on a
local GPU, showing potential for usable autonomous child-robot speech
interactions.

自动语音识别在成人英语语音方面显示出超人的表现，但在儿童语音方面表现不佳。最新的数据驱动语音识别技术包括 Transformer 架构和前所未有的训练数据量，可能会为儿童语音识别和面向儿童的社交机器人应用带来突破。我们重新审视了 2017 年的一项关于儿童语音识别的研究，并表明性能确实有所提高，新晋的 OpenAI Whisper 相比领先的商业云服务表现明显更好。尽管转录还不完美，但最佳模型可以正确识别 60.3% 的句子，除了一些小的语法差异，使用本地 GPU 运行的亚秒级转录时间显示出可用于自主儿童 - 机器人语音交互的潜力。