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

As in any data-intensive domain, collecting highquality To fill this gap, this paper explores the use datasets is a fundamental and costly prerequisite of SFMs to automatize the validation of crowdsourced for the development of speech-processing speech data. To this aim, we investigate the applications. Traditional methods heavily rely on employment of off-the-shelf SFMs such as Whisper human workforce, whose costs, as data collection and SeamlessM4T (Radford et al., 2022; Communication scales, are hard to sustain. In the quest for scalable et al., 2023), along with machine translation solutions to tackle this problem, crowdsourcing (MT) models and grapheme-to-phoneme conversion emerged as a viable option that also enables the coverage (G2P). Through experiments on French, of diverse populations (Cefkin et al., 2014; German, and Korean data, we test the integration Poesio et al., 2017). Due to the variable quality of of SFMs and crowdsourcing to reduce validation crowd-sourced data, validation methods that discard costs while preserving final data quality. Our results low-quality contributions are essential to build show that leveraging SFMs yields a cost reduction reliable datasets (Negri et al., 2011; Sabou et al., by over 40%, while maintaining high data quality, 2014; Chittilappilly et al., 2016). This need is exacerbated significantly improving the efficiency and scalability in the collection of speech-text pairs, where of crowd-sourced speech data collection.