Paper Citation Record · LEDGER

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings

As of 22 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 2 inbound Pith citation observations for arXiv:2506.17690.

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pith.paper-citation-record.v1
2506.17690 v1

Coverage vector

measured 39 of 39 reference resolution

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measured 41 of 41 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:34:20.642492Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T07:57:44.414977Z

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 3d543db5-6ca1-44d5-adcd-eee6f7427f48 · outbound

This paper cites an unresolved cited work.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Unresolved cited work

Reference 1

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Observation 8a5b2c6f-d30a-4812-ae58-262c5d11c13b · outbound

This paper cites Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings

Reference 2

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Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Unresolved cited work

Reference 3

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Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Unresolved cited work

Reference 4

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This paper cites Pre-trained models For meanpooling and subsampling as described in Section 2.2, we consider four pre-trained self-supervised models from the wav2vec2.0 and HuBERT families [25, 26].

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Pre-trained models For meanpooling and subsampling as described in Section 2.2, we consider four pre-trained self-supervised models from the wav2vec2.0 and HuBERT families [25, 26]

Reference 5

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Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Unresolved cited work

Reference 6

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This paper cites The AWEs produced by this transformer are then used to encode speech in the target language.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings The AWEs produced by this transformer are then used to encode speech in the target language

Reference 7

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Observation 8dacbc54-fec8-45db-b49d-9e91ef3641cb · outbound

This paper cites We also thank the DW Ackermann Bursary Fund and Telkom South Africa for support.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings We also thank the DW Ackermann Bursary Fund and Telkom South Africa for support

Reference 8

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Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Unresolved cited work

Reference 9

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Observation 4332a72a-2fe1-4f04-b85c-ef225b762c32 · outbound

This paper cites Feature learning for efficient ASR-free keyword spot- ting in low-resource languages,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Feature learning for efficient ASR-free keyword spot- ting in low-resource languages,

Reference 10

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Observation bed53b08-7505-4862-8f19-349c4b3f6a1a · outbound

This paper cites Unsupervised spoken keyword spotting via segmental DTW on Gaussian posteriorgrams,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Unsupervised spoken keyword spotting via segmental DTW on Gaussian posteriorgrams,

Reference 11

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Observation e0db45ca-d94d-4684-854d-55bb59a0b33c · outbound

This paper cites Fast ASR-free and almost zero-resource keyword spotting using DTW and CNNs for humanitarian monitoring,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Fast ASR-free and almost zero-resource keyword spotting using DTW and CNNs for humanitarian monitoring,

Reference 12

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This paper cites ASR- free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings ASR- free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages,

Reference 13

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Observation e7288748-c049-407a-b89f-f231af7c5896 · outbound

This paper cites Feature exploration for almost zero-resource asr-free keyword spotting using a multilingual bottleneck extractor and correspondence autoencoders,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Feature exploration for almost zero-resource asr-free keyword spotting using a multilingual bottleneck extractor and correspondence autoencoders,

Reference 14

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Observation ad6a8738-10bc-472c-a1ff-7ec658bd4244 · outbound

This paper cites Low-resource ASR-free key- word spotting using listen-and-confirm,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Low-resource ASR-free key- word spotting using listen-and-confirm,

Reference 15

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Observation 804d738b-6a46-48bb-8f65-26d18411dd83 · outbound

This paper cites Fixed- dimensional acoustic embeddings of variable-length segments in low-resource settings,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Fixed- dimensional acoustic embeddings of variable-length segments in low-resource settings,

Reference 16

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Observation a781b04c-5f33-4d16-92ce-c2f583a9355e · outbound

This paper cites Acoustic span embeddings for multilingual query-by-example search,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Acoustic span embeddings for multilingual query-by-example search,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:34:30.445728Z

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Observation 97784a63-56dd-43d9-968b-3bfd39891c87 · outbound

This paper cites Audio word2vec: Unsupervised learning of audio segment repre- sentations using sequence-to-sequence autoencoder,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Audio word2vec: Unsupervised learning of audio segment repre- sentations using sequence-to-sequence autoencoder,

Reference 18

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Observation 6717668b-312e-4459-bd6c-3e9d2ff05ebc · outbound

This paper cites Truly unsupervised acoustic word embeddings using weak top-down constraints in encoder-decoder models,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Truly unsupervised acoustic word embeddings using weak top-down constraints in encoder-decoder models,

Reference 19

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Observation 9bad9526-f1eb-4797-8c6c-d73949a21c3e · outbound

This paper cites Discriminative acoustic word embed- dings: recurrent neural network-based approaches,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Discriminative acoustic word embed- dings: recurrent neural network-based approaches,

Reference 20

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This paper cites A comparison of self-supervised speech representations as input features for unsupervised acoustic word embeddings,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings A comparison of self-supervised speech representations as input features for unsupervised acoustic word embeddings,

Reference 21

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This paper cites Acoustic word em- beddings for zero-resource languages using self-supervised con- trastive learning and multilingual adaptation,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Acoustic word em- beddings for zero-resource languages using self-supervised con- trastive learning and multilingual adaptation,

Reference 22

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This paper cites Self-supervised acoustic word embedding learning via correspondence transformer encoder,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Self-supervised acoustic word embedding learning via correspondence transformer encoder,

Reference 23

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Observation 60395cf7-1c9e-4b26-98c5-3705aada9f0e · outbound

This paper cites Improved acous- tic word embeddings for zero-resource languages using multilin- gual transfer,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Improved acous- tic word embeddings for zero-resource languages using multilin- gual transfer,

Reference 24

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Observation decd7506-a03f-463e-b863-adbdcf692fa6 · outbound

This paper cites Multilingual transfer of acoustic word embeddings improves when training on languages related to the target zero-resource language,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Multilingual transfer of acoustic word embeddings improves when training on languages related to the target zero-resource language,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:34:28.247058Z

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Observation 6e092636-7599-4b40-bde4-5e21f789b065 · outbound

This paper cites Analyzing acoustic word embeddings from pre-trained self-supervised speech models,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Analyzing acoustic word embeddings from pre-trained self-supervised speech models,

Reference 26

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Observation f97bfc77-4c22-476e-99a3-82bfc860af6f · outbound

This paper cites Acoustic word embeddings for untranscribed target languages with con- tinued pretraining and learned pooling,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Acoustic word embeddings for untranscribed target languages with con- tinued pretraining and learned pooling,

Reference 27

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This paper cites Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili,

Reference 28

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This paper cites Attention is all you need,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Attention is all you need,

Reference 29

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This paper cites BERT: pre- training of deep bidirectional transformers for language under- standing,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings BERT: pre- training of deep bidirectional transformers for language under- standing,

Reference 30

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This paper cites The NCHLT speech corpus of the South African lan- guages,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings The NCHLT speech corpus of the South African lan- guages,

Reference 31

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

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Common Voice: A massively-multilingual speech corpus,

Reference 32

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

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings wav2vec 2.0: A framework for self-supervised learning of speech representa- tions,

Reference 33

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This paper cites Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units,

Reference 34

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

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings XLS-R: Self-supervised cross-lingual speech representation learning at scale,

Reference 35

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Observation e1ebc566-077b-44f6-8e16-d2b9d3f65eeb · outbound

This paper cites mhubert-147: A compact multilingual hubert model,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings mhubert-147: A compact multilingual hubert model,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:34:25.645718Z

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Observation 9d38f61b-bfb8-4f90-9c5d-8ea5f7128488 · outbound

This paper cites Montreal forced aligner: Trainable text-speech align- ment using kaldi,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Montreal forced aligner: Trainable text-speech align- ment using kaldi,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:34:25.386880Z

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Observation 22cee52e-e14e-4138-8649-7e78f68f08bf · outbound

This paper cites Rapid evaluation of speech representations for spoken term discovery,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Rapid evaluation of speech representations for spoken term discovery,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:34:25.192818Z

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Observation 30b677a2-061c-479b-9ee9-ea0a3f86fec5 · outbound

This paper cites Layer-wise analysis of a self-supervised speech representation model,.

Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings Layer-wise analysis of a self-supervised speech representation model,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:34:24.949123Z

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Pith citing papers

Observation 8a5b2c6f-d30a-4812-ae58-262c5d11c13b · inbound