Paper Citation Record · LEDGER

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models

As of 8 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2506.01133.

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

pith.paper-citation-record.v1
2506.01133 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

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measured 47 of 47 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:55:19.199833Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T11:55:23.785586Z

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation f91442a7-2fc5-4d1a-b385-d937125a5a34 · outbound

This paper cites Among these, large language models (LLMs) have demonstrated emer- gent capabilities once thought to require human intelligence.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Among these, large language models (LLMs) have demonstrated emer- gent capabilities once thought to require human intelligence

Reference 1

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This paper cites From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models

Reference 2

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This paper cites We investigate both unimodal and multimodal models, focusing on HuBERT, BERT, Seamless M4T, and SpeechT5.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models We investigate both unimodal and multimodal models, focusing on HuBERT, BERT, Seamless M4T, and SpeechT5

Reference 3

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From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models w_1", "w_2

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From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Unresolved cited work

Reference 5

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From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Unresolved cited work

Reference 6

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This paper cites Sparks of artificial general intelligence: Early experiments with GPT-4,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Sparks of artificial general intelligence: Early experiments with GPT-4,

Reference 7

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This paper cites Holistic Evaluation of Language Models.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Holistic Evaluation of Language Models

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models LLaMA: Open and Efficient Foundation Language Models

Reference 9

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This paper cites Qwen2-Audio Technical Report.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Qwen2-Audio Technical Report

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This paper cites HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding

Reference 11

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This paper cites Chatgpt makes medicine easy to swallow: An exploratory case study on simplified radiology reports,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Chatgpt makes medicine easy to swallow: An exploratory case study on simplified radiology reports,

Reference 12

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This paper cites Where do you know what you know? the representation of semantic knowledge in the human brain,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Where do you know what you know? the representation of semantic knowledge in the human brain,

Reference 13

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This paper cites Discovering latent concepts learned in BERT,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Discovering latent concepts learned in BERT,

Reference 14

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Observation 44907d11-e6c7-4baa-aaa6-ef31c6a3d378 · outbound

This paper cites Asking without telling: Exploring latent ontologies in contextual representations,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Asking without telling: Exploring latent ontologies in contextual representations,

Reference 15

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

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Acoustic word embeddings for untranscribed target languages with con- tinued pretraining and learned pooling,

Reference 16

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This paper cites Scaling up discovery of latent concepts in deep NLP models,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Scaling up discovery of latent concepts in deep NLP models,

Reference 17

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

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units,

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From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models BERT: Pre- training of deep bidirectional transformers for language under- standing,

Reference 19

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This paper cites Seamlessm4t: Massively multilingual & multimodal machine translation,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Seamlessm4t: Massively multilingual & multimodal machine translation,

Reference 20

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This paper cites Speecht5: Unified-modal encoder-decoder pre-training for spoken language processing,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Speecht5: Unified-modal encoder-decoder pre-training for spoken language processing,

Reference 21

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This paper cites Building a large annotated corpus of English: The Penn Treebank,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Building a large annotated corpus of English: The Penn Treebank,

Reference 22

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This paper cites Introduction to the CoNLL-2000 shared task chunking,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Introduction to the CoNLL-2000 shared task chunking,

Reference 23

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This paper cites The parallel meaning bank: Towards a multilingual corpus of translations annotated with compositional meaning representations,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models The parallel meaning bank: Towards a multilingual corpus of translations annotated with compositional meaning representations,

Reference 24

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This paper cites Recursive deep models for semantic composition- ality over a sentiment treebank,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Recursive deep models for semantic composition- ality over a sentiment treebank,

Reference 25

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This paper cites Lib- rispeech: an asr corpus based on public domain audio books,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Lib- rispeech: an asr corpus based on public domain audio books,

Reference 26

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This paper cites Neurox library for neuron analysis of deep nlp models,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Neurox library for neuron analysis of deep nlp models,

Reference 27

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From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models GLUE: A multi-task benchmark and analysis platform for natu- ral language understanding,

Reference 28

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From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Slue: New benchmark tasks for spoken language un- derstanding evaluation on natural speech,

Reference 29

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This paper cites What happens to BERT embeddings during fine-tuning?.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models What happens to BERT embeddings during fine-tuning?

Reference 30

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From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models How transfer learning im- pacts linguistic knowledge in deep NLP models?

Reference 31

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From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Can llms facilitate interpreta- tion of pre-trained language models?

Reference 32

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From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models BERT rediscovers the classical NLP pipeline,

Reference 33

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From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models What does BERT learn about the structure of language?

Reference 34

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This paper cites Analyzing encoded concepts in transformer language models,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Analyzing encoded concepts in transformer language models,

Reference 35

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This paper cites On the transforma- tion of latent space in fine-tuned nlp models,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models On the transforma- tion of latent space in fine-tuned nlp models,

Reference 36

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This paper cites What do end-to- end speech models learn about speaker, language and channel information? a layer-wise and neuron-level analysis,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models What do end-to- end speech models learn about speaker, language and channel information? a layer-wise and neuron-level analysis,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:23.925647Z

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Observation 40d3f00f-dc15-40de-ae50-6e4e6fbd9f3a · outbound

This paper cites What Do Speech Foundation Models Not Learn About Speech?.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models What Do Speech Foundation Models Not Learn About Speech?

Reference 38

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This paper cites Speech representation analysis based on inter-and intra-model similarities,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Speech representation analysis based on inter-and intra-model similarities,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:23.905500Z

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This paper cites How do Multimodal Foundation Models Encode Text and Speech? An Analysis of Cross-Lingual and Cross-Modal Representations.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models How do Multimodal Foundation Models Encode Text and Speech? An Analysis of Cross-Lingual and Cross-Modal Representations

Reference 40

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Observation 9df1dfe6-f19e-459b-bb87-64ea8f1580c0 · outbound

This paper cites Human-like linguistic biases in neural speech models: Phonetic categorization and phonotactic constraints in wav2vec2. 0,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Human-like linguistic biases in neural speech models: Phonetic categorization and phonotactic constraints in wav2vec2. 0,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:23.885809Z

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Observation 37ba5095-d0ca-4102-970f-e089cc26f7a4 · outbound

This paper cites Probing self- supervised speech models for phonetic and phonemic informa- tion: A case study in aspiration,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Probing self- supervised speech models for phonetic and phonemic informa- tion: A case study in aspiration,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:23.864140Z

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Observation 32d511d8-d2e6-4675-82d4-02a10a2f1c09 · outbound

This paper cites SD-HuBERT: Sentence-level self-distillation in- duces syllabic organization in hubert,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models SD-HuBERT: Sentence-level self-distillation in- duces syllabic organization in hubert,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:23.846073Z

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This paper cites Phonetic analysis of self- supervised representations of english speech,.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Phonetic analysis of self- supervised representations of english speech,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:23.826922Z

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Observation 1bf89544-a4f6-4900-a853-94601fe19a48 · outbound

This paper cites Self-Supervised Speech Representations are More Phonetic than Semantic.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models Self-Supervised Speech Representations are More Phonetic than Semantic

Reference 45

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This paper cites What do self- supervised speech models know about words?.

From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models What do self- supervised speech models know about words?

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:23.809257Z

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Pith citing papers

Observation ff3e0a17-38ff-4eb3-88ca-76261b5660ad · inbound