A perceptual listening audit of 2,280 utterance pairs sets a cosine-similarity threshold of 0.354 for removing likely different-speaker utterances from Common Voice client IDs.
Bengali Common Voice Speech Dataset for Automatic Speech Recognition
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abstract
Bengali is one of the most spoken languages in the world with over 300 million speakers globally. Despite its popularity, research into the development of Bengali speech recognition systems is hindered due to the lack of diverse open-source datasets. As a way forward, we have crowdsourced the Bengali Common Voice Speech Dataset, which is a sentence-level automatic speech recognition corpus. Collected on the Mozilla Common Voice platform, the dataset is part of an ongoing campaign that has led to the collection of over 400 hours of data in 2 months and is growing rapidly. Our analysis shows that this dataset has more speaker, phoneme, and environmental diversity compared to the OpenSLR Bengali ASR dataset, the largest existing open-source Bengali speech dataset. We present insights obtained from the dataset and discuss key linguistic challenges that need to be addressed in future versions. Additionally, we report the current performance of a few Automatic Speech Recognition (ASR) algorithms and set a benchmark for future research.
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Quantifying and Reducing Speaker Heterogeneity within the Common Voice Corpus for Phonetic Analysis
A perceptual listening audit of 2,280 utterance pairs sets a cosine-similarity threshold of 0.354 for removing likely different-speaker utterances from Common Voice client IDs.