Child Speech Recognition in Human-Robot Interaction: Problem Solved?
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912125596729344 |
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| author | Janssens, Ruben Verhelst, Eva Abbo, Giulio Antonio Ren, Qiaoqiao Bernal, Maria Jose Pinto Belpaeme, Tony |
| author_facet | Janssens, Ruben Verhelst, Eva Abbo, Giulio Antonio Ren, Qiaoqiao Bernal, Maria Jose Pinto Belpaeme, Tony |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17394 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Child Speech Recognition in Human-Robot Interaction: Problem Solved? Janssens, Ruben Verhelst, Eva Abbo, Giulio Antonio Ren, Qiaoqiao Bernal, Maria Jose Pinto Belpaeme, Tony Computation and Language Human-Computer Interaction Robotics 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. |
| title | Child Speech Recognition in Human-Robot Interaction: Problem Solved? |
| topic | Computation and Language Human-Computer Interaction Robotics |
| url | https://arxiv.org/abs/2404.17394 |