# Updated Corpora and Benchmarks for Long-Form Speech Recognition

arXiv: https://arxiv.org/abs/2309.15013 · HTML: https://mlsanity.com/paper/2309.15013/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 41 references that are in the map.
- First posted on arXiv 26 Sep 2023.

## Builds on (most cited first, 29 of 29)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning](https://mlsanity.com/paper/1609.06773/) (2016)
- [TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation](https://mlsanity.com/paper/1805.04699/) (2018)
- [Recognizing long-form speech using streaming end-to-end models](https://mlsanity.com/paper/1910.11455/) (2019)
- [Earnings-21: A Practical Benchmark for ASR in the Wild](https://mlsanity.com/paper/2104.11348/) (2021)
- [Earnings-22: A Practical Benchmark for Accents in the Wild](https://mlsanity.com/paper/2203.15591/) (2022)
- [Pruned RNN-T for fast, memory-efficient ASR training](https://mlsanity.com/paper/2206.13236/) (2022)
- [A comparison of end-to-end models for long-form speech recognition](https://mlsanity.com/paper/1911.02242/) (2019)
- [Lhotse: a speech data representation library for the modern deep learning ecosystem](https://mlsanity.com/paper/2110.12561/) (2021)
- [Dialog-context aware end-to-end speech recognition](https://mlsanity.com/paper/1808.02171/) (2018)
- [RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions](https://mlsanity.com/paper/2005.03271/) (2020)
- [End-to-End Automatic Speech Recognition Integrated With CTC-Based Voice Activity Detection](https://mlsanity.com/paper/2002.00551/) (2020)
- [E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR](https://mlsanity.com/paper/2204.10749/) (2022)
- [Large-scale Language Model Rescoring on Long-form Data](https://mlsanity.com/paper/2306.08133/) (2023)
- [Input Length Matters: Improving RNN-T and MWER Training for Long-form Telephony Speech Recognition](https://mlsanity.com/paper/2110.03841/) (2021)
- [Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts](https://mlsanity.com/paper/2306.01031/) (2023)
- [Improving RNN-T ASR Accuracy Using Context Audio](https://mlsanity.com/paper/2011.10538/) (2020)
- [Leveraging Acoustic Contextual Representation by Audio-textual Cross-modal Learning for Conversational ASR](https://mlsanity.com/paper/2207.01039/) (2022)
- [LongFNT: Long-form Speech Recognition with Factorized Neural Transducer](https://mlsanity.com/paper/2211.09412/) (2022)
- [Towards Effective and Compact Contextual Representation for Conformer Transducer Speech Recognition Systems](https://mlsanity.com/paper/2306.13307/) (2023)
- [LSTM-LM with Long-Term History for First-Pass Decoding in Conversational Speech Recognition](https://mlsanity.com/paper/2010.11349/) (2020)
- [Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition](https://mlsanity.com/paper/2210.15876/) (2022)
- [Context Dependent RNNLM for Automatic Transcription of Conversations](https://mlsanity.com/paper/2008.03517/) (2020)

## Built on by (most cited first, 10 of 10)

- [Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence](https://mlsanity.com/paper/2604.24954/) (2026)
- [On the Effects of Heterogeneous Data Sources on Speech-to-Text Foundation Models](https://mlsanity.com/paper/2406.09282/) (2024)
- [Longer is (Not Necessarily) Stronger: Punctuated Long-Sequence Training for Enhanced Speech Recognition and Translation](https://mlsanity.com/paper/2409.05601/) (2024)
- [Whisper-CD: Accurate Long-Form Speech Recognition using Multi-Negative Contrastive Decoding](https://mlsanity.com/paper/2603.06193/) (2026)
- [Exploring SSL Discrete Speech Features for Zipformer-based Contextual ASR](https://mlsanity.com/paper/2409.08797/) (2024)
- [Efficiently Identifying Low-Quality Language Subsets in Multilingual Datasets: A Case Study on a Large-Scale Multilingual Audio Dataset](https://mlsanity.com/paper/2410.04292/) (2024)
- [Accurate, fast, cheap: Choose three. Replacing Multi-Head-Attention with Bidirectional Recurrent Attention for Long-Form ASR](https://mlsanity.com/paper/2506.19761/) (2025)
- [Segmental Attention Decoding With Long Form Acoustic Encodings](https://mlsanity.com/paper/2512.14652/) (2025)
- [MURMUR: An Efficient Inference System for Long-Form ASR](https://mlsanity.com/paper/2606.01483/) (2026)
- [FBK's Long-form SpeechLLMs for IWSLT 2026 Instruction Following](https://mlsanity.com/paper/2606.26819/) (2026)

## Abstract (arXiv)

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.