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

Updated Corpora and Benchmarks for Long-Form Speech Recognition