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• W3143186397 abstract "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 this https URL." @default.
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• W3143186397 date "2021-04-05" @default.
• W3143186397 modified "2023-09-26" @default.
• W3143186397 title "SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition" @default.
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