@inproceedings{32d2ce149dd94a7ba1cf89d5b4411c03,
title = "A comparative analysis between Conformer-Transducer, Whisper, and wav2vec2 for improving the child speech recognition",
abstract = "Automatic Speech Recognition (ASR) systems have progressed significantly in their performance on adult speech data; however, transcribing child speech remains challenging due to the acoustic differences in the characteristics of child and adult voices. This work aims to explore the potential of adapting state-of-the-art Conformer-transducer models to child speech to improve child speech recognition performance. Furthermore, the results are compared with those of self-supervised wav2vec2 models and semi-supervised multi-domain Whisper models that were previously finetuned on the same data. We demonstrate that finetuning Conformer-transducer models on child speech yields significant improvements in ASR performance on child speech, compared to the non-finetuned models. We also show Whisper and wav2vec2 adaptation on different child speech datasets. Our detailed comparative analysis shows that wav2vec2 provides the most consistent performance improvements among the three methods studied.",
keywords = "Automatic Speech Recognition, Child Speech Recognition, CMU-Kids, Conformer-transducer, MyST, PF-STAR, wav2vec2, Whisper model",
author = "Andrei Barcovschi and Rishabh Jain and Peter Corcoran",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th International Conference on Speech Technology and Human-Computer Dialogue, SpeD 2023 ; Conference date: 25-10-2023 Through 27-10-2023",
year = "2023",
doi = "10.1109/SpeD59241.2023.10314867",
language = "English",
series = "2023 International Conference on Speech Technology and Human-Computer Dialogue, SpeD 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "42--47",
editor = "Dragos Burileanu",
booktitle = "2023 International Conference on Speech Technology and Human-Computer Dialogue, SpeD 2023",
}