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Supplementary Resources and Analysis for Automatic Speech Recognition Systems Trained on the Loquacious Dataset Nick Rossenbach∗†, Robin Schmitt∗†, Tina Raissi∗, Simon Berger∗†, Larissa Kleppel∗, Ralf Schlüter∗† ∗RWTH Aachen University, †AppTek.ai Aachen, Germany {lastname}@ml.rwth-aachen.de Abstract 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 experimen- tal results indicate that the Loquacious dataset offers a valuable study case for a variety of common challenges in ASR. Keywords: Loquacious, language model, pronunciation lexicon, speech recognition, reference baselines 1. Introduction In order to compare ASR systems across scientific publications, it is necessary to clearly define the training and testing conditions and data used. For this purpose, many benchmarks and correspond- ing datasets have been published in the past. No- table datasets for academic use covering English language are: Switchboard (Godfrey et al., 1992), LibriSpeech (Panayotov et al., 2015) or TED-Lium (Hernandez et al., 2018). Especially LibriSpeech, with more than 1700 citations in 2024 alone1, is dominating ASR research. However, due to the increase in computation power, more and more re- search is performed without any constraints on the used data. This generally yields better results, but limits scientific comparability. On the other hand, existing datasets only provide a smaller amount of data, and are often not realistic in the diversity of au- dio and text conditions. Also, some datasets are not directly accessible, were removed from public ac- cess after publication2, or have restricted licenses. The Loquacious dataset (Parcollet et al., 2025) was published with the idea in mind to replace older datasets, offering multiple benefits. The important highlights are: • A diverse selection of data sources containing both read and spontaneous speech • Three different training subsets, consisting of 250, 2.5k, and 25k hours, with 250 and 2.5k having uniformly balanced data sources • Defined dev and test sets for every data source 1according to Google Scholar, 15th Oct. 2025 2The TED terms of use disallow machine learning usage, existing datasets based on TED were taken offline • A strong and consistent text normalization • Open licensing for academia and industry Such aspects make the Loquacious dataset a promising replacement for older standard datasets, with the 2.5k hours subset having a similar magni- tude as the 960 hours of LibriSpeech. Moreover, due to similar text normalization, it is straightfor- ward to use the dataset with existing LibriSpeech- oriented pipelines. As the original release of Loquacious only in- cluded training and test data, we aim to extend the available material with count-based LMs, pro- nunciation lexica, and pre-trained G2P models. Such resources exist for LibriSpeech3 and other datasets, and are relevant for the comparison of ASR pipelines that require such resources. The introduction of a pronunciation lexicon allows for explicit decoder representations via lexical trees or finite state machines. These representations can integrate word-level and lexical information together with model specific label topologies (Mohri et al., 2002). This type of constraint could benefit the ASR performance, for example under domain mismatch conditions (Raissi et al., 2025). Moreover, the align- ment of models using a blank-free label topology with phonemic units and silence modeling offers the advantage of providing word and phoneme bound- aries unambiguously. Prior works also show that phoneme-based models are able to provide align- ments with more accurate time information relative to the evidence in the signal, compared to the byte- pair encoding (BPE) based models (Rousso et al., 2024; Jiang et al., 2023). 3https://openslr.org/11/ arXiv:2512.17915v1 [cs.CL] 27 Nov 2025
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https://arxiv.org/pdf/2512.17915
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2512.17915
| 1
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10.940000534057617, "top": 245.40496826171875, "width": 26.67999839782715, "height": 40.0}]
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[]
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2512.17915_page_2
| "1.1. Contributions With this work, we benchmark several ASR archi- tectures with different label to(...TRUNCATED)
| [-0.03953583911061287,-0.14064234495162964,0.057217247784137726,-0.03852261230349541,-0.041813749819(...TRUNCATED)
|
https://arxiv.org/pdf/2512.17915
|
2512.17915
| 2
| "[{\"text\": \"1.1.\", \"left\": 72.0, \"top\": 64.3185043334961, \"width\": 18.345840454101562, \"h(...TRUNCATED)
|
[]
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2512.17915_page_3
| "Table 1: Perplexities and OOV percentage of the different count-based LMs on the respective dev set(...TRUNCATED)
| [-0.024678321555256844,-0.17939873039722443,-0.08653436601161957,-0.016259046271443367,-0.0219895374(...TRUNCATED)
|
https://arxiv.org/pdf/2512.17915
|
2512.17915
| 3
| "[{\"text\": \"Table\", \"left\": 71.69100189208984, \"top\": 72.17292785644531, \"width\": 24.14553(...TRUNCATED)
|
[]
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2512.17915_page_4
| "Table 2: Recognition results for BPE ASR systems that work without additional LMs or lexicon. Abbre(...TRUNCATED)
| [-0.06810492277145386,-0.06766447424888611,-0.0348125658929348,-0.04632832109928131,0.04795836284756(...TRUNCATED)
|
https://arxiv.org/pdf/2512.17915
|
2512.17915
| 4
| "[{\"text\": \"Table\", \"left\": 71.69100189208984, \"top\": 72.17292785644531, \"width\": 23.59940(...TRUNCATED)
|
[]
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2512.17915_page_5
| "4.2. Count-based Language Model We use the 216k words vocabulary from Section 2.1 and the pruned 4-(...TRUNCATED)
| [0.004336571786552668,-0.115930937230587,0.04757677763700485,0.030209969729185104,0.0312812402844429(...TRUNCATED)
|
https://arxiv.org/pdf/2512.17915
|
2512.17915
| 5
| "[{\"text\": \"4.2.\", \"left\": 72.0, \"top\": 64.3185043334961, \"width\": 18.345840454101562, \"h(...TRUNCATED)
|
[]
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2512.17915_page_6
| "Table 3: Recognition results for different BPE ASR systems to compare the effect of vocabulary rest(...TRUNCATED)
| [-0.003550386056303978,-0.0599285252392292,-0.049036905169487,-0.05048362910747528,0.044207546859979(...TRUNCATED)
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https://arxiv.org/pdf/2512.17915
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2512.17915
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| "[{\"text\": \"Table\", \"left\": 71.69100189208984, \"top\": 72.17292785644531, \"width\": 23.42227(...TRUNCATED)
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2512.17915_page_7
| "Table 5: Recognition results for different ASR systems comparing BPE to phoneme performance. Result(...TRUNCATED)
| [-0.04637373983860016,-0.059191904962062836,-0.04366108775138855,-0.07091904431581497,0.006375262513(...TRUNCATED)
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https://arxiv.org/pdf/2512.17915
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| "[{\"text\": \"Table\", \"left\": 71.69100189208984, \"top\": 72.17292785644531, \"width\": 23.42227(...TRUNCATED)
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2512.17915_page_8
| "Table 9: Detailed WERs in terms of substitutions, insertions, and deletions on the LibriSpeech and (...TRUNCATED)
| [-0.06503663957118988,-0.09698916226625443,0.05206318199634552,-0.08792991936206818,-0.0255831666290(...TRUNCATED)
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https://arxiv.org/pdf/2512.17915
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| "[{\"text\": \"Table\", \"left\": 71.69100189208984, \"top\": 72.17292785644531, \"width\": 23.42227(...TRUNCATED)
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2512.17915_page_9
| "mance, and for certain research or applications it might be beneficial to also explore such systems(...TRUNCATED)
| [-0.042436376214027405,-0.022550757974386215,0.024699769914150238,0.02385099045932293,0.012524338439(...TRUNCATED)
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https://arxiv.org/pdf/2512.17915
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| "[{\"text\": \"mance,\", \"left\": 72.0, \"top\": 64.80091857910156, \"width\": 33.12471008300781, \(...TRUNCATED)
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2512.17915_page_10
| "Kenneth Heafield. 2011. KenLM: Faster and smaller language model queries. In Proceed- ings of the S(...TRUNCATED)
| [-0.05252157896757126,-0.12413886934518814,0.04521544650197029,0.00046796395326964557,0.023742327466(...TRUNCATED)
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| "[{\"text\": \"Kenneth\", \"left\": 72.0, \"top\": 64.80091857910156, \"width\": 37.405792236328125,(...TRUNCATED)
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[]
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