Speech Foundation Models and Crowdsourcing for Efficient, High-Quality Data Collection
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912158948786176 |
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| author | Lee, Beomseok Gaido, Marco Calapodescu, Ioan Besacier, Laurent Negri, Matteo |
| author_facet | Lee, Beomseok Gaido, Marco Calapodescu, Ioan Besacier, Laurent Negri, Matteo |
| contents | While crowdsourcing is an established solution for facilitating and scaling the collection of speech data, the involvement of non-experts necessitates protocols to ensure final data quality. To reduce the costs of these essential controls, this paper investigates the use of Speech Foundation Models (SFMs) to automate the validation process, examining for the first time the cost/quality trade-off in data acquisition. Experiments conducted on French, German, and Korean data demonstrate that SFM-based validation has the potential to reduce reliance on human validation, resulting in an estimated cost saving of over 40.0% without degrading final data quality. These findings open new opportunities for more efficient, cost-effective, and scalable speech data acquisition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11978 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Speech Foundation Models and Crowdsourcing for Efficient, High-Quality Data Collection Lee, Beomseok Gaido, Marco Calapodescu, Ioan Besacier, Laurent Negri, Matteo Computation and Language Sound Audio and Speech Processing While crowdsourcing is an established solution for facilitating and scaling the collection of speech data, the involvement of non-experts necessitates protocols to ensure final data quality. To reduce the costs of these essential controls, this paper investigates the use of Speech Foundation Models (SFMs) to automate the validation process, examining for the first time the cost/quality trade-off in data acquisition. Experiments conducted on French, German, and Korean data demonstrate that SFM-based validation has the potential to reduce reliance on human validation, resulting in an estimated cost saving of over 40.0% without degrading final data quality. These findings open new opportunities for more efficient, cost-effective, and scalable speech data acquisition. |
| title | Speech Foundation Models and Crowdsourcing for Efficient, High-Quality Data Collection |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2412.11978 |