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

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.

5 pages, 1 figure. Submitted to INTERSPEECH 2021

References in corpus (5)

• Sequence Transduction with Recurrent Neural Networks
• Conformer: Convolution-augmented Transformer for Speech Recognition
• GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio
• RNN Approaches to Text Normalization: A Challenge
• Cross-Language Transfer Learning, Continuous Learning, and Domain Adaptation for End-to-End Automatic Speech Recognition

Cited by in corpus (2)

• Lhotse: a speech data representation library for the modern deep learning ecosystem
• Scalable Data Annotation Pipeline for High-Quality Large Speech Datasets Development