Updated Corpora and Benchmarks for Long-Form Speech Recognition
Jennifer Drexler Fox Desh Raj Natalie Delworth Quinn McNamara Corey Miller Migüel Jetté
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.