mlsanityData to 7 Oct 2026

RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks

arXiv 1608.00895 ↗First posted 2 Aug 2016cs.LG cs.CL cs.NEcited by 16

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Abstract (arXiv)

In this work we release our extensible and easily configurable neural network training software. It provides a rich set of functional layers with a particular focus on efficient training of recurrent neural network topologies on multiple GPUs. The source of the software package is public and freely available for academic research purposes and can be used as a framework or as a standalone tool which supports a flexible configuration. The software allows to train state-of-the-art deep bidirectional long short-term memory (LSTM) models on both one dimensional data like speech or two dimensional data like handwritten text and was used to develop successful submission systems in several evaluation campaigns.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Aug 2016.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 4; 2024: 1; 2025: 2; 2026: 1.

16 papers.

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  1. 2021Recent Advances in End-to-End Automatic Speech Recognitioncited by 138
  2. 2023End-to-End Speech Recognition: A Surveycited by 127
  3. 2021Differentiable Allophone Graphs for Language-Universal Speech Recognitioncited by 10
  4. 2021Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architecturescited by 8
  5. 2024MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decodercited by 7
  6. 2021Equivalence of Segmental and Neural Transducer Modeling: A Proof of Conceptcited by 5
  7. 2022Efficient Utilization of Large Pre-Trained Models for Low Resource ASRcited by 4
  8. 2023On the Relevance of Phoneme Duration Variability of Synthesized Training Data for Automatic Speech Recognitioncited by 4
  9. 2022AppTek's Submission to the IWSLT 2022 Isometric Spoken Language Translation Taskcited by 3
  10. 2022Development of Hybrid ASR Systems for Low Resource Medical Domain Conversational Telephone Speechcited by 3
  11. 2023Unsupervised Pre-Training for Vietnamese Automatic Speech Recognition in the HYKIST Projectcited by 2
  12. 2023End-to-End Training of a Neural HMM with Label and Transition Probabilitiescited by 1
4 more
  1. 2025Running Conventional Automatic Speech Recognition on Memristor Hardware: A Simulated Approachcited by 1
  2. 2021Automatic Learning of Subword Dependent Model Scalescited by 0
  3. 2025Analyzing the Importance of Blank for CTC-Based Knowledge Distillationcited by 0
  4. 2026Positional Encoding in the Context of Memristor-Based Analog Computation for Automatic Speech Recognitioncited by 0