Phone Segmentation and Recognition through Phonological Activation Mapping | Paper Citation Record

Observation 0b2569cc-1701-45e9-9e9b-cbf5526f1a9d · outbound

This paper cites TIMIT acoustic-phonetic continuous speech corpus,.

Phone Segmentation and Recognition through Phonological Activation Mapping TIMIT acoustic-phonetic continuous speech corpus,

Reference 1

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Observation cbdf787f-d46e-471f-bcd4-a481e475216e · outbound

This paper cites Phonetic segmentation of the UCLA phonetics lab archive,.

Phone Segmentation and Recognition through Phonological Activation Mapping Phonetic segmentation of the UCLA phonetics lab archive,

Reference 2

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Observation 2682be51-89fd-4442-bfe5-e6f50dd42e6a · outbound

This paper cites an unresolved cited work.

Phone Segmentation and Recognition through Phonological Activation Mapping Unresolved cited work

Reference 3

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Observation 20bd48be-b8d9-4da1-9805-aae37df39e33 · outbound

This paper cites EduSpeak®: A speech recognition and pronunciation scoring toolkit for computer-aided language learning applications,.

Phone Segmentation and Recognition through Phonological Activation Mapping EduSpeak®: A speech recognition and pronunciation scoring toolkit for computer-aided language learning applications,

Reference 4

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Observation 3cadde25-72c4-443e-88db-8c24a531a70a · outbound

This paper cites PRiSM: Benchmarking phone realization in speech models,.

Phone Segmentation and Recognition through Phonological Activation Mapping PRiSM: Benchmarking phone realization in speech models,

Reference 5

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Observation 42c2382a-46fe-4f94-839a-29454a71b84e · outbound

This paper cites Tusom2021: A Phonetically Transcribed Speech Dataset from an Endangered Language for Universal Phone Recognition Experiments,.

Phone Segmentation and Recognition through Phonological Activation Mapping Tusom2021: A Phonetically Transcribed Speech Dataset from an Endangered Language for Universal Phone Recognition Experiments,

Reference 6

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Observation ae27c8d6-4788-4354-b4a8-f2197d521e24 · outbound

This paper cites Prosodic abx: A language-agnostic method for measuring prosodic contrast in speech representations,.

Phone Segmentation and Recognition through Phonological Activation Mapping Prosodic abx: A language-agnostic method for measuring prosodic contrast in speech representations,

Reference 7

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Observation 5cd114aa-0b21-43b5-ab44-990d572a6637 · outbound

This paper cites Speech Playground: An Interactive Tool for Speech Analysis and Comparison.

Phone Segmentation and Recognition through Phonological Activation Mapping Speech Playground: An Interactive Tool for Speech Analysis and Comparison

Reference 8

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Observation 72c23509-da29-4dec-b46e-2f6f336583a5 · outbound

This paper cites Towards language-agnostic stipa: Universal phonetic transcription to support language documentation at scale,.

Phone Segmentation and Recognition through Phonological Activation Mapping Towards language-agnostic stipa: Universal phonetic transcription to support language documentation at scale,

Reference 9

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Observation b0c425a4-f668-4b6b-bf2f-9ab3e08fb5de · outbound

This paper cites Language documentation twenty-five years on,.

Phone Segmentation and Recognition through Phonological Activation Mapping Language documentation twenty-five years on,

Reference 10

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Observation 9c50e989-3ab5-469c-8e1c-70df7aa53f53 · outbound

This paper cites The buckeye corpus of conversational speech: labeling conventions and a test of transcriber reliability,.

Phone Segmentation and Recognition through Phonological Activation Mapping The buckeye corpus of conversational speech: labeling conventions and a test of transcriber reliability,

Reference 11

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Observation b582588b-5386-4d63-a67c-630e58fde0cc · outbound

This paper cites Reliability studies in broad and narrow phonetic transcription,.

Phone Segmentation and Recognition through Phonological Activation Mapping Reliability studies in broad and narrow phonetic transcription,

Reference 12

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Observation 3aea93c9-e3be-4ae9-9dee-48817784ad88 · outbound

This paper cites Simple and Effective Zero-shot Cross- lingual Phoneme Recognition,.

Phone Segmentation and Recognition through Phonological Activation Mapping Simple and Effective Zero-shot Cross- lingual Phoneme Recognition,

Reference 13

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Observation 9e7a11bd-e9a0-4904-bb0f-6a5f16967a7b · outbound

This paper cites ZIPA: A family of efficient models for multilingual phone recognition,.

Phone Segmentation and Recognition through Phonological Activation Mapping ZIPA: A family of efficient models for multilingual phone recognition,

Reference 14

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Observation d896be05-28c5-4b51-842c-ac7f1cdb01c3 · outbound

This paper cites POWSM: A phonetic open whisper-style speech foundation model,.

Phone Segmentation and Recognition through Phonological Activation Mapping POWSM: A phonetic open whisper-style speech foundation model,

Reference 15

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Observation 8f382439-53b3-46a0-a037-905bf6188a5d · outbound

This paper cites An Empirical Recipe for Universal Phone Recognition,.

Phone Segmentation and Recognition through Phonological Activation Mapping An Empirical Recipe for Universal Phone Recognition,

Reference 16

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Observation e0fcbc95-6206-4bcb-8d9a-3a76479ff1d1 · outbound

This paper cites Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,.

Phone Segmentation and Recognition through Phonological Activation Mapping Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,

Reference 17

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Observation d71d8d26-c0a9-44ad-b5e7-95ab3b6ba834 · outbound

This paper cites Attention is all you need,.

Phone Segmentation and Recognition through Phonological Activation Mapping Attention is all you need,

Reference 18

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Observation 6e02882f-f6c6-4c59-be0e-420d9434effe · outbound

This paper cites Attention-based models for speech recognition,.

Phone Segmentation and Recognition through Phonological Activation Mapping Attention-based models for speech recognition,

Reference 19

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Observation 366d6377-5d3c-40e7-beda-79e4da88ef33 · outbound

This paper cites Phoneme Segmentation Using Self-Supervised Speech Models,.

Phone Segmentation and Recognition through Phonological Activation Mapping Phoneme Segmentation Using Self-Supervised Speech Models,

Reference 20

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Observation 24a42d0f-faa9-4286-87f4-b34286fb4421 · outbound

This paper cites Phone-to-audio alignment without text: A semi-supervised approach,.

Phone Segmentation and Recognition through Phonological Activation Mapping Phone-to-audio alignment without text: A semi-supervised approach,

Reference 21

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Observation 291a6fa1-4aff-434f-88c4-0f4c1adb336c · outbound

This paper cites Explore wav2vec 2.0 for mispronunciation detection.

Phone Segmentation and Recognition through Phonological Activation Mapping Explore wav2vec 2.0 for mispronunciation detection

Reference 22

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Observation 7ace7755-68d9-481e-b5b6-6f2ec5f77765 · outbound

This paper cites Speech intelligibility assessment of dysarthric speech by using goodness of pronunciation with uncertainty quantification,.

Phone Segmentation and Recognition through Phonological Activation Mapping Speech intelligibility assessment of dysarthric speech by using goodness of pronunciation with uncertainty quantification,

Reference 23

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Observation e62587dc-5889-4549-a380-1144facb1037 · outbound

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

Phone Segmentation and Recognition through Phonological Activation Mapping wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 24

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Observation 247b429c-17fe-4e84-9bb9-05f82370812c · outbound

This paper cites HuBERT: Self-supervised speech representation learning by masked prediction of hidden units,.

Phone Segmentation and Recognition through Phonological Activation Mapping HuBERT: Self-supervised speech representation learning by masked prediction of hidden units,

Reference 25

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Observation 8a3f6c9f-9a57-49bf-ab01-1a46e9af62b8 · outbound

This paper cites WavLM: Large-scale self-supervised pre- training for full stack speech processing,.

Phone Segmentation and Recognition through Phonological Activation Mapping WavLM: Large-scale self-supervised pre- training for full stack speech processing,

Reference 26

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Observation 3edab171-99dc-45a4-9c0e-c8653b174df3 · outbound

This paper cites [b]=[d]- [t]+[p]: Self-supervised speech models discover phonological vector arithmetic,.

Phone Segmentation and Recognition through Phonological Activation Mapping [b]=[d]- [t]+[p]: Self-supervised speech models discover phonological vector arithmetic,

Reference 27

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Observation 72aaabfd-4aed-4361-a018-2d4c890e0663 · outbound

This paper cites Self- supervised speech models encode phonetic context via position-dependent orthogonal subspaces,.

Phone Segmentation and Recognition through Phonological Activation Mapping Self- supervised speech models encode phonetic context via position-dependent orthogonal subspaces,

Reference 28

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Observation c2096f22-434b-483e-ac0e-38f0ced3079d · outbound

This paper cites Opening the Black Box of wav2vec Feature Encoder.

Phone Segmentation and Recognition through Phonological Activation Mapping Opening the Black Box of wav2vec Feature Encoder

Reference 29

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Observation cda3997d-79e2-4ad0-a069-e7009c84dfbb · outbound

This paper cites Analysing discrete self supervised speech representation for spoken language modeling,.

Phone Segmentation and Recognition through Phonological Activation Mapping Analysing discrete self supervised speech representation for spoken language modeling,

Reference 30

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Observation 7115bc31-bec1-4eb0-baec-057d7f1ae2fc · outbound

This paper cites Leveraging allophony in self-supervised speech models for atypical pronunciation assessment,.

Phone Segmentation and Recognition through Phonological Activation Mapping Leveraging allophony in self-supervised speech models for atypical pronunciation assessment,

Reference 31

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Observation c9db6dda-5733-4661-a53c-67ac1b1393e0 · outbound

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

Phone Segmentation and Recognition through Phonological Activation Mapping Layer-wise analysis of a self- supervised speech representation model,

Reference 32

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Observation 69a49c40-034a-4d1a-a84e-d2211ae6b573 · outbound

This paper cites What do Speech Foundation Models Learn? Analysis and Applications.

Phone Segmentation and Recognition through Phonological Activation Mapping What do Speech Foundation Models Learn? Analysis and Applications

Reference 33

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Observation 5e84c68d-d62d-427b-9152-8f6cf4f17737 · outbound

This paper cites Panphon: A resource for mapping ipa segments to articulatory feature vectors,.

Phone Segmentation and Recognition through Phonological Activation Mapping Panphon: A resource for mapping ipa segments to articulatory feature vectors,

Reference 34

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Observation 1bb34012-b65d-450e-9516-b48b3cd8d28b · outbound

This paper cites Multi-level acoustic segmentation of continuous speech,.

Phone Segmentation and Recognition through Phonological Activation Mapping Multi-level acoustic segmentation of continuous speech,

Reference 35

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Observation f858bf78-081d-4041-a8b0-444c7371d6e8 · outbound

This paper cites Segmentation and modeling in segment- based recognition,.

Phone Segmentation and Recognition through Phonological Activation Mapping Segmentation and modeling in segment- based recognition,

Reference 36

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Observation 84aeab93-7341-4799-bca9-c3e22176971a · outbound

This paper cites Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation,.

Phone Segmentation and Recognition through Phonological Activation Mapping Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation,

Reference 37

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Observation bb3a14a2-ac85-40c5-bee4-bf597568ca3b · outbound

This paper cites A simple hmm with self-supervised represen- tations for phone segmentation,.

Phone Segmentation and Recognition through Phonological Activation Mapping A simple hmm with self-supervised represen- tations for phone segmentation,

Reference 38

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Observation a49084cc-7f62-4fea-a81a-02eab721a34b · outbound

This paper cites Unsupervised Speech Segmentation and Variable Rate Representation Learning Using Segmental Contrastive Predictive Coding,.

Phone Segmentation and Recognition through Phonological Activation Mapping Unsupervised Speech Segmentation and Variable Rate Representation Learning Using Segmental Contrastive Predictive Coding,

Reference 39

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Observation 2f51eb97-9548-470b-b473-df654e632fa5 · outbound

This paper cites Universal phone recognition with a multilingual allophone system,.

Phone Segmentation and Recognition through Phonological Activation Mapping Universal phone recognition with a multilingual allophone system,

Reference 40

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Observation 83317e2f-1840-4159-aaab-38a152d0f89f · outbound

This paper cites Allophant: Cross-lingual Phoneme Recognition with Articulatory Attributes,.

Phone Segmentation and Recognition through Phonological Activation Mapping Allophant: Cross-lingual Phoneme Recognition with Articulatory Attributes,

Reference 41

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Observation 3c66455a-767b-46a5-91c9-b3f2e5eab489 · outbound

This paper cites Text-Independent Phone-to-Audio Alignment Leveraging SSL (TIPAA-SSL) Pre-Trained Model Latent Representation and Knowledge Transfer,.

Phone Segmentation and Recognition through Phonological Activation Mapping Text-Independent Phone-to-Audio Alignment Leveraging SSL (TIPAA-SSL) Pre-Trained Model Latent Representation and Knowledge Transfer,

Reference 42

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Observation e887a5b0-5305-404d-8f0e-2ccfc18a278f · outbound

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

Phone Segmentation and Recognition through Phonological Activation Mapping Self-Supervised Speech Representations are More Phonetic than Semantic,

Reference 43

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Observation fa2e5b3b-d491-40f4-affc-83619330854e · outbound

This paper cites SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,.

Phone Segmentation and Recognition through Phonological Activation Mapping SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,

Reference 44

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Observation 407ba5ae-31de-453a-aa61-d82713ed45b2 · outbound

This paper cites A new text-independent method for phoneme segmentation,.

Phone Segmentation and Recognition through Phonological Activation Mapping A new text-independent method for phoneme segmentation,

Reference 45

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Observation 0856be8c-0903-4bc8-94d4-0aad2f0e93b4 · outbound

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

Phone Segmentation and Recognition through Phonological Activation Mapping Montreal forced aligner: Trainable text-speech alignment using kaldi,

Reference 46

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Observation a5e8040c-f40f-47d8-bb09-11111b34987b · outbound

This paper cites XLSR Inclusive English Speech-to-IPA,.

Phone Segmentation and Recognition through Phonological Activation Mapping XLSR Inclusive English Speech-to-IPA,

Reference 47

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Observation 6827d785-ad25-4f0e-a24f-522b86df9c74 · outbound

This paper cites L2-ARCTIC: A Non-native English Speech Corpus,.

Phone Segmentation and Recognition through Phonological Activation Mapping L2-ARCTIC: A Non-native English Speech Corpus,

Reference 48

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Observation af6fc254-ee22-4d9b-bb2b-f251c42f805c · outbound

This paper cites Speech Accent Archive,.

Phone Segmentation and Recognition through Phonological Activation Mapping Speech Accent Archive,

Reference 49

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Observation 4128b828-76c0-4cdd-9175-cf73e1209663 · outbound

This paper cites Building a time-aligned cross-linguistic reference corpus from language documentation data (DoReCo),.

Phone Segmentation and Recognition through Phonological Activation Mapping Building a time-aligned cross-linguistic reference corpus from language documentation data (DoReCo),

Reference 50

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Observation 6edbb141-0841-4c03-948a-3739e4545238 · outbound

This paper cites Scaling human and g2p supervision for robust phonetic transcription,.

Phone Segmentation and Recognition through Phonological Activation Mapping Scaling human and g2p supervision for robust phonetic transcription,

Reference 51

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Observation bfe146eb-12dc-431e-89b9-3b3da55fadd2 · outbound

This paper cites Global TIMIT learner simple english,.

Phone Segmentation and Recognition through Phonological Activation Mapping Global TIMIT learner simple english,

Reference 52

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Observation a980baf9-78e0-49ad-a7dc-64dd303576df · outbound

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Phone Segmentation and Recognition through Phonological Activation Mapping Global TIMIT learner treebank english,

Reference 53

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Observation e0ea52e4-e6f7-4ac2-810b-ad47ba3e7e05 · outbound

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Phone Segmentation and Recognition through Phonological Activation Mapping Global TIMIT Thai,

Reference 54

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Observation 68a9c72a-a699-4da2-b4eb-5a1451d20392 · outbound

This paper cites Dysarthric speech corpus in Tamil for rehabilitation research,.

Phone Segmentation and Recognition through Phonological Activation Mapping Dysarthric speech corpus in Tamil for rehabilitation research,

Reference 55

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Observation bfd23527-10e3-4cf8-a955-10e5ce9dd5c2 · outbound

This paper cites A weighted speaker-specific confusion transducer-based augmentative and alternative speech communication aid for dysarthric speakers,.

Phone Segmentation and Recognition through Phonological Activation Mapping A weighted speaker-specific confusion transducer-based augmentative and alternative speech communication aid for dysarthric speakers,

Reference 56

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Observation b678ea2a-0e69-402b-8589-c90b6f77f856 · outbound

This paper cites The TORGO database of acoustic and articulatory speech from speakers with dysarthria,.

Phone Segmentation and Recognition through Phonological Activation Mapping The TORGO database of acoustic and articulatory speech from speakers with dysarthria,

Reference 57

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Observation 249a241f-1f6c-4960-a88b-548fb9c8422d · outbound

This paper cites Dysarthria detection and severity assessment using rhythm-based metrics.

Phone Segmentation and Recognition through Phonological Activation Mapping Dysarthria detection and severity assessment using rhythm-based metrics

Reference 58

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Observation 81c23fa9-c196-40dd-accf-5c81ec332d48 · outbound

This paper cites An improved speech segmentation quality measure: the r-value,.

Phone Segmentation and Recognition through Phonological Activation Mapping An improved speech segmentation quality measure: the r-value,

Reference 59

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Observation 66565d3e-77f4-4548-9ad6-47aebe653174 · outbound

This paper cites English mfa acoustic model v3.1.0,.

Phone Segmentation and Recognition through Phonological Activation Mapping English mfa acoustic model v3.1.0,

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Observation c0c58d9a-bc06-49b4-9c68-d306f2fc0628 · outbound

This paper cites V oxcommunis corpus,.

Phone Segmentation and Recognition through Phonological Activation Mapping V oxcommunis corpus,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-07-13T00:57:35.442569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1f6c27d-80d9-4f6d-9265-6b31b35bfb1c · outbound

This paper cites Thai mfa acoustic model v3.0.0,.

Phone Segmentation and Recognition through Phonological Activation Mapping Thai mfa acoustic model v3.0.0,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-13T00:57:35.442569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9e0c1f6-5bb1-4edb-9d92-d70de0442bc6 · outbound

This paper cites Wav2Gloss: Generating Interlinear Glossed Text from Speech,.

Phone Segmentation and Recognition through Phonological Activation Mapping Wav2Gloss: Generating Interlinear Glossed Text from Speech,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-07-13T00:57:35.442569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 720973fc-af65-488e-ae0f-d563e623b9b9 · outbound

This paper cites XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale,.

Phone Segmentation and Recognition through Phonological Activation Mapping XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-07-13T00:57:35.442569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d08cc2fa-334e-4eee-9842-f37c909ae23d · outbound

This paper cites Towards unsupervised phone and word segmentation using self-supervised vector-quantized neural networks,.

Phone Segmentation and Recognition through Phonological Activation Mapping Towards unsupervised phone and word segmentation using self-supervised vector-quantized neural networks,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-07-13T00:57:35.442569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers