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2512.17915_page_1 | 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 publi... | [
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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) | [] | |
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) | [] | |
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) | [] | |
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) | [] | |
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) | https://arxiv.org/pdf/2512.17915 | 2512.17915 | 6 | "[{\"text\": \"Table\", \"left\": 71.69100189208984, \"top\": 72.17292785644531, \"width\": 23.42227(...TRUNCATED) | [] | |
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) | https://arxiv.org/pdf/2512.17915 | 2512.17915 | 7 | "[{\"text\": \"Table\", \"left\": 71.69100189208984, \"top\": 72.17292785644531, \"width\": 23.42227(...TRUNCATED) | [] | |
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) | https://arxiv.org/pdf/2512.17915 | 2512.17915 | 8 | "[{\"text\": \"Table\", \"left\": 71.69100189208984, \"top\": 72.17292785644531, \"width\": 23.42227(...TRUNCATED) | [] | |
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) | https://arxiv.org/pdf/2512.17915 | 2512.17915 | 9 | "[{\"text\": \"mance,\", \"left\": 72.0, \"top\": 64.80091857910156, \"width\": 33.12471008300781, \(...TRUNCATED) | [] | |
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) | https://arxiv.org/pdf/2512.17915 | 2512.17915 | 10 | "[{\"text\": \"Kenneth\", \"left\": 72.0, \"top\": 64.80091857910156, \"width\": 37.405792236328125,(...TRUNCATED) | [] |
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