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SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition

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arxiv 2104.02014 v2 pith:55TY6UNY submitted 2021-04-05 cs.CL eess.AS

SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition

Patrick K. O'Neill , Vitaly Lavrukhin , Somshubra Majumdar , Vahid Noroozi , Yuekai Zhang , Oleksii Kuchaiev , Jagadeesh Balam , Yuliya Dovzhenko
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Keenan Freyberg Michael D. Shulman Boris Ginsburg Shinji Watanabe Georg Kucsko
This is my paper
classification cs.CL eess.AS
keywords modelsacousticcorpusdatasetsend-to-endformattedfullyhours
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In the English speech-to-text (STT) machine learning task, acoustic models are conventionally trained on uncased Latin characters, and any necessary orthography (such as capitalization, punctuation, and denormalization of non-standard words) is imputed by separate post-processing models. This adds complexity and limits performance, as many formatting tasks benefit from semantic information present in the acoustic signal but absent in transcription. Here we propose a new STT task: end-to-end neural transcription with fully formatted text for target labels. We present baseline Conformer-based models trained on a corpus of 5,000 hours of professionally transcribed earnings calls, achieving a CER of 1.7. As a contribution to the STT research community, we release the corpus free for non-commercial use at https://datasets.kensho.com/datasets/scribe.

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Cited by 6 Pith papers

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