Paper Citation Record · LEDGER

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish

As of 8 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2506.01156.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.01156 v1

Coverage vector

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Reference resolution

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External citation measurements

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Outbound references

Observation e9dc0ec5-9279-47ad-9ae7-fc6895e11ec9 · outbound

This paper cites an unresolved cited work.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:56:19.960488Z

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This paper cites Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish

Reference 2

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This paper cites For MD, we use Recall ( T R T R+F A), Precision ( T R T R+F R), and their harmonic mean F1 as our metrics [5].

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish For MD, we use Recall ( T R T R+F A), Precision ( T R T R+F R), and their harmonic mean F1 as our metrics [5]

Reference 3

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This paper cites The predicted labels’ proba- bilities, P , are obtained by softmax functionσ: P = σ(z/T ).

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish The predicted labels’ proba- bilities, P , are obtained by softmax functionσ: P = σ(z/T )

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:56:19.912825Z

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This paper cites Divide P by the scaled probability of top-1.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Divide P by the scaled probability of top-1

Reference 5

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This paper cites partially correct.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish partially correct

Reference 6

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Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Unresolved cited work

Reference 7

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This paper cites We relied entirely on L1 data and tested on a minimal L2 ASR corpus.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish We relied entirely on L1 data and tested on a minimal L2 ASR corpus

Reference 8

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This paper cites Technology-enhanced foreign and second-language learning of Nordic languages.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Technology-enhanced foreign and second-language learning of Nordic languages

Reference 9

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Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Unresolved cited work

Reference 10

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This paper cites Kautonen and M.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Kautonen and M

Reference 11

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This paper cites Ideologies of standardisation: Finland Swedish and Swedish-language Finland,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Ideologies of standardisation: Finland Swedish and Swedish-language Finland,

Reference 12

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This paper cites Swedish quantity: Cen- tral Standard Swedish and Fenno-Swedish,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Swedish quantity: Cen- tral Standard Swedish and Fenno-Swedish,

Reference 13

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This paper cites Mispronunciation detection and diagnosis using deep neural networks: A systematic review,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Mispronunciation detection and diagnosis using deep neural networks: A systematic review,

Reference 14

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This paper cites L2-ARCTIC: A non-native English speech corpus,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish L2-ARCTIC: A non-native English speech corpus,

Reference 15

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This paper cites speechocean762: An open-source non- native English speech corpus for pronunciation assessment,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish speechocean762: An open-source non- native English speech corpus for pronunciation assessment,

Reference 16

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This paper cites Phonetic-level mis- pronunciation detection in non-native Swedish speech,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Phonetic-level mis- pronunciation detection in non-native Swedish speech,

Reference 17

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This paper cites Developing a benchmark for pronun- ciation feedback: Creation of a phonemically annotated speech corpus of isiZulu language learner speech,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Developing a benchmark for pronun- ciation feedback: Creation of a phonemically annotated speech corpus of isiZulu language learner speech,

Reference 18

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This paper cites Mispronunciation detection without nonna- tive training data,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Mispronunciation detection without nonna- tive training data,

Reference 19

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This paper cites CaptainA - a mobile app for practising Finnish pronunciation,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish CaptainA - a mobile app for practising Finnish pronunciation,

Reference 20

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This paper cites Finland Swedish automatic speech recogni- tion,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Finland Swedish automatic speech recogni- tion,

Reference 21

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This paper cites Automatic speaking assessment of spontaneous L2 Finnish and Swedish,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Automatic speaking assessment of spontaneous L2 Finnish and Swedish,

Reference 22

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This paper cites Ivars, Dialekter och sm ˚astadsspr˚ak, ser.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Ivars, Dialekter och sm ˚astadsspr˚ak, ser

Reference 23

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This paper cites Changing contexts and shifting paradigms in pro- nunciation teaching,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Changing contexts and shifting paradigms in pro- nunciation teaching,

Reference 24

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Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Unresolved cited work

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This paper cites An overview of spoken language technology for education,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish An overview of spoken language technology for education,

Reference 26

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This paper cites Corrective feedback and teacher development,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Corrective feedback and teacher development,

Reference 27

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This paper cites wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,

Reference 28

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This paper cites XLS-R: Self-supervised cross-lingual speech representation learning at scale,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish XLS-R: Self-supervised cross-lingual speech representation learning at scale,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:56:19.196965Z

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This paper cites A study on fine-tuning wav2vec2.0 model for the task of mispronunciation detection and diagnosis,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish A study on fine-tuning wav2vec2.0 model for the task of mispronunciation detection and diagnosis,

Reference 30

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This paper cites CTC-segmentation of large corpora for German end-to-end speech recognition,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish CTC-segmentation of large corpora for German end-to-end speech recognition,

Reference 31

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This paper cites Con- nectionist temporal classification: Labelling unsegmented se- quence data with recurrent neural networks,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Con- nectionist temporal classification: Labelling unsegmented se- quence data with recurrent neural networks,

Reference 32

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This paper cites Connectionist temporal classifica- tion with maximum entropy regularization,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Connectionist temporal classifica- tion with maximum entropy regularization,

Reference 33

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This paper cites Explore wav2vec 2.0 for mispronunciation detection,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Explore wav2vec 2.0 for mispronunciation detection,

Reference 34

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This paper cites Phone-level pronunciation scoring and assessment for interactive language learning,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Phone-level pronunciation scoring and assessment for interactive language learning,

Reference 35

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This paper cites On calibra- tion of modern neural networks,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish On calibra- tion of modern neural networks,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:56:18.914683Z

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This paper cites Common voice: A massively-multilingual speech corpus,.

Mispronunciation Detection Without L2 Pronunciation Dataset in Low-Resource Setting: A Case Study in Finland Swedish Common voice: A massively-multilingual speech corpus,

Reference 37

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Pith citing papers

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