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arXiv · 2309.15013

Updated Corpora and Benchmarks for Long-Form Speech Recognition

Jennifer Drexler Fox·Desh Raj·Natalie Delworth·Quinn McNamara·Corey Miller·Migüel Jetté
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Abstract

The vast majority of ASR research uses corpora in which both the training and test data have been pre-segmented into utterances. In most real-word ASR use-cases, however, test audio is not segmented, leading to a mismatch between inference-time conditions and models trained on segmented utterances. In this paper, we re-release three standard ASR corpora - TED-LIUM 3, Gigapeech, and VoxPopuli-en - with updated transcription and alignments to enable their use for long-form ASR research. We use these reconstituted corpora to study the train-test mismatch problem for transducers and attention-based encoder-decoders (AEDs), confirming that AEDs are more susceptible to this issue. Finally, we benchmark a simple long-form training for these models, showing its efficacy for model robustness under this domain shift.

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Jennifer Drexler Fox, Desh Raj, Natalie Delworth, Quinn McNamara, Corey Miller, Migüel Jetté. 2023-09-26. Updated Corpora and Benchmarks for Long-Form Speech Recognition. https://arxiv.org/abs/2309.15013

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