Speech Recognition for Automatically Assessing Afrikaans and isiXhosa Preschool Oral Narratives
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arXiv
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| Format: | Preprint |
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2025
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| author | Jacobs, Christiaan Smith, Annelien Klop, Daleen Klejch, Ondřej de Wet, Febe Kamper, Herman |
| author_facet | Jacobs, Christiaan Smith, Annelien Klop, Daleen Klejch, Ondřej de Wet, Febe Kamper, Herman |
| contents | We develop automatic speech recognition (ASR) systems for stories told by Afrikaans and isiXhosa preschool children. Oral narratives provide a way to assess children's language development before they learn to read. We consider a range of prior child-speech ASR strategies to determine which is best suited to this unique setting. Using Whisper and only 5 minutes of transcribed in-domain child speech, we find that additional in-domain adult data (adult speech matching the story domain) provides the biggest improvement, especially when coupled with voice conversion. Semi-supervised learning also helps for both languages, while parameter-efficient fine-tuning helps on Afrikaans but not on isiXhosa (which is under-represented in the Whisper model). Few child-speech studies look at non-English data, and even fewer at the preschool ages of 4 and 5. Our work therefore represents a unique validation of a wide range of previous child-speech ASR strategies in an under-explored setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06478 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Speech Recognition for Automatically Assessing Afrikaans and isiXhosa Preschool Oral Narratives Jacobs, Christiaan Smith, Annelien Klop, Daleen Klejch, Ondřej de Wet, Febe Kamper, Herman Audio and Speech Processing Computation and Language Sound We develop automatic speech recognition (ASR) systems for stories told by Afrikaans and isiXhosa preschool children. Oral narratives provide a way to assess children's language development before they learn to read. We consider a range of prior child-speech ASR strategies to determine which is best suited to this unique setting. Using Whisper and only 5 minutes of transcribed in-domain child speech, we find that additional in-domain adult data (adult speech matching the story domain) provides the biggest improvement, especially when coupled with voice conversion. Semi-supervised learning also helps for both languages, while parameter-efficient fine-tuning helps on Afrikaans but not on isiXhosa (which is under-represented in the Whisper model). Few child-speech studies look at non-English data, and even fewer at the preschool ages of 4 and 5. Our work therefore represents a unique validation of a wide range of previous child-speech ASR strategies in an under-explored setting. |
| title | Speech Recognition for Automatically Assessing Afrikaans and isiXhosa Preschool Oral Narratives |
| topic | Audio and Speech Processing Computation and Language Sound |
| url | https://arxiv.org/abs/2501.06478 |