子ども音声認識は解決したのか？人間とロボットの相互作用における再評価
Child Speech Recognition in Human-Robot Interaction: Problem Solved?
2017年の子ども音声認識研究を再検証し、OpenAI Whisperが商用サービスを上回り、特定フレーズでさらに精度が向上することを示した。
著者: Ruben Janssens, Eva Verhelst, Giulio Antonio Abbo, Qiaoqiao Ren, Maria Jose Pinto Bernal, Tony Belpaeme
分類: cs.CL, cs.HC, cs.RO
原文アブストラクト
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. Performance improves even more in highly structured interactions when priming models with specific phrases. 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.
