mlsanityData to 14 Oct 2026

Supplementary Resources and Analysis for Automatic Speech Recognition Systems Trained on the Loquacious Dataset

arXiv 2512.17915 ↗First posted 27 Nov 2025cs.CL cs.LGcited by 2

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Abstract (arXiv)

The recently published Loquacious dataset aims to be a replacement for established English automatic speech recognition (ASR) datasets such as LibriSpeech or TED-Lium. The main goal of the Loquacious dataset is to provide properly defined training and test partitions across many acoustic and language domains, with an open license suitable for both academia and industry. To further promote the benchmarking and usability of this new dataset, we present additional resources in the form of n-gram language models (LMs), a grapheme-to-phoneme (G2P) model and pronunciation lexica, with open and public access. Utilizing those additional resources we show experimental results across a wide range of ASR architectures with different label units and topologies. Our initial experimental results indicate that the Loquacious dataset offers a valuable study case for a variety of common challenges in ASR.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 32 references that are in the map. First posted on arXiv 27 Nov 2025.

Builds on

15 of 32 references.

References · 15

  1. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
  2. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,863
  3. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,380
  4. 2018Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidatescited by 639
  5. 2015Listen, Attend and Spellcited by 543
  6. 2018TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptationcited by 226
  7. 2020Hybrid Autoregressive Transducer (hat)cited by 108
  8. 2020Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognitioncited by 80
  9. 2018RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognitioncited by 30
  10. 2024Hallucinations in Neural Automatic Speech Recognition: Identifying Errors and Hallucinatory Modelscited by 27
  11. 2024Tradition or Innovation: A Comparison of Modern ASR Methods for Forced Alignmentcited by 21
  12. 2025Loquacious Set: 25,000 Hours of Transcribed and Diverse English Speech Recognition Data for Research and Commercial Usecited by 9
3 more
  1. 2025NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decodingcited by 7
  2. 2020Context-Dependent Acoustic Modeling without Explicit Phone Clusteringcited by 3
  3. 2025Analysis of Domain Shift across ASR Architectures via TTS-Enabled Separation of Target Domain and Acoustic Conditionscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026A Native-Reference Coordinate Geometry for L2 Pronunciation Deviation Using Self-Supervised Speech Modelscited by 1
  2. 2026Positional Encoding in the Context of Memristor-Based Analog Computation for Automatic Speech Recognitioncited by 0