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    <abstract>We evaluate the impact of recent improvements in Automatic Speech Recognition (ASR) on transcribing Estonian children’s speech. Our research focuses on fine-tuning large ASR models with a 10-hour Estonian children’s speech dataset to create accurate transcriptions. Our results show that large pre-trained models hold great potential when fine-tuned first with a more substantial Estonian adult speech corpus and then further trained with children’s speech.</abstract>
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%0 Conference Proceedings
%T Automatic Transcription for Estonian Children’s Speech
%A Luhtaru, Agnes
%A Jaaska, Rauno
%A Kruusamäe, Karl
%A Fishel, Mark
%Y Alumäe, Tanel
%Y Fishel, Mark
%S Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2023
%8 May
%I University of Tartu Library
%C Tórshavn, Faroe Islands
%F luhtaru-etal-2023-automatic
%X We evaluate the impact of recent improvements in Automatic Speech Recognition (ASR) on transcribing Estonian children’s speech. Our research focuses on fine-tuning large ASR models with a 10-hour Estonian children’s speech dataset to create accurate transcriptions. Our results show that large pre-trained models hold great potential when fine-tuned first with a more substantial Estonian adult speech corpus and then further trained with children’s speech.
%U https://aclanthology.org/2023.nodalida-1.70
%P 705-709

Markdown (Informal)

[Automatic Transcription for Estonian Children’s Speech](https://aclanthology.org/2023.nodalida-1.70) (Luhtaru et al., NoDaLiDa 2023)

• Automatic Transcription for Estonian Children’s Speech (Luhtaru et al., NoDaLiDa 2023)

ACL

• Agnes Luhtaru, Rauno Jaaska, Karl Kruusamäe, and Mark Fishel. 2023. Automatic Transcription for Estonian Children’s Speech. In Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pages 705–709, Tórshavn, Faroe Islands. University of Tartu Library.