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

• URL: http://arxiv.org/abs/2104.02014v2
• Date: Tue, 6 Apr 2021 04:22:48 GMT
• Title: SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition
• Authors: Patrick K. O'Neill, Vitaly Lavrukhin, Somshubra Majumdar, Vahid Noroozi, Yuekai Zhang, Oleksii Kuchaiev, Jagadeesh Balam, Yuliya Dovzhenko, Keenan Freyberg, Michael D. Shulman, Boris Ginsburg, Shinji Watanabe, and Georg Kucsko
• Abstract summary: In the English speech-to-text (STT) machine learning task, acoustic models are conventionally trained on uncased Latin characters. 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.
• Score: 38.96077127913159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://datasets.kensho.com/datasets/scribe.

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