Automatic Validation of the Non-Validated S panish Speech Data of Common Voice 17.0 (2025)

Abstract: Mozilla Common Voice is a crowdsourced project that aims to create a public, multilingual dataset of voice recordings for training speech recognition models. In Common Voice, anyone can contribute by donating or validating recordings in various languages. However, despite the availability of many recordings in certain languages, a significant percentage remains unvalidated by users. This is the case for Spanish, where in version 17.0 of Common Voice, 75% of the 2,220 hours of recordings are unvalidated. In this work, we used the Whisper recognizer to automatically validate approximately 784 hours of recordings which are more than the 562 hours validated by users. To verify the accuracy of the validation, we developed a speech recognition model based on a version of NVIDIA-NeMo’s Parakeet, which does not have an official Spanish version. Our final model achieved a WER of less than 4% on the test and validation splits of Common Voice 17.0. Both the model and the speech corpus are publicly available on Hugging Face.

## introduction developing automatic speech recognition (asr) systems requires extensive labeled speech data, which is costly and time-consuming to annotate manually. crowdsourcing and automated methods help address these challenges by enabling efficient and consistent validation of data quality. for instance, @xcite used the kaldi toolkit @xcite to prepare the ted-lium corpus, while @xcite introduced a toolkit for automatic validation based on criteria like audio quality and transcription ac-curacy. automated pipelines have also been applied in domain-specific scenarios @xcite , audio-visual speech recognition @xcite , and multilingual asr systems from parliamentary archives @xcite @xcite . in this work, we employ openai's whisper asr model to automatically validate the data by comparing the automatic transcription to a reference, which may or may not accurately reflect the content of the speech recording. ## the corpus the corpus "spanish common voice v17.0 split other automatically verified," foot_0 as its name suggests, is the result of the automatic validation of the split called "other," which is part of the spanish version of the common voice 17.0 corpus (cv17 for short). the corpus contains 784 hours and 50 minutes of audio across 581,680 recordings, surpassing the size of the "validated" category in cv17, which contains only 562 hours. of these, 53 hours are allocated to the "test" and "dev" splits, while the remaining 509 hours belong to the "train" split. in comparison to the original cv17, our corpus contains only a single split called "other." ## acoustic models an indirect way to assess the correctness of our validation process is to evaluate how our validated recordings perform when training a real asr model, as faulty data would hinder the production of a good acoustic model. for this purpose, we fine-tuned distinct models based on nvidia's parakeet architecture, as described in section 4.1. it is important to note that, to the best of our knowledge, the official model (trained by nvidia) based on the specific parakeet architecture we use in this work is only available in english; so, we chose this parakeet architecture with the hope of making a meaningful contribution to the language technologies community. ## conclusions and further work crowdsourcing platforms are vital for asr development, offering affordable and diverse data collection. however, manual validation limits their efficiency. this study demonstrated the potential of automatic validation for the spanish subset of common voice 17.0 (cv17) using a whisperbased asr system. our best parakeet model trained with the extended dataset, "cv17 combined", outperformed both openai's whisper and whisper-large-v3, showcasing the benefits of automated validation. future work could explore applying this approach to other datasets (e.g., voxforge 5 ) and languages, especially low-resource ones, which could gain significantly from automated dataset expansion despite potential model performance challenges.