# Speech Foundation Models and Crowdsourcing for Efficient, High-Quality Data Collection

arXiv: https://arxiv.org/abs/2412.11978 · HTML: https://mlsanity.com/paper/2412.11978/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [If in a Crowdsourced Data Annotation Pipeline, a GPT-4](https://mlsanity.com/paper/2402.16795/) (2024)
- [Speech-MASSIVE: A Multilingual Speech Dataset for SLU and Beyond](https://mlsanity.com/paper/2408.03900/) (2024)
- [Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech](https://mlsanity.com/paper/2402.16321/) (2024)
- [Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction](https://mlsanity.com/paper/2312.15616/) (2023)

## Built on by (most cited first, 1 of 1)

- [Ouvia: A User-centered Framework for Measuring Usability of Speech Translation in Real-World Communication Scenarios](https://mlsanity.com/paper/2606.06177/) (2026)

## Abstract (arXiv)

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.