Paper Citation Record · LEDGER

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis

As of 23 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2506.01129.

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

pith.paper-citation-record.v1
2506.01129 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:53:59.361709Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:53:54.861247Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T11:53:59.918982Z

Reference resolution

50 of 50 outbound references displayed

verified exact3
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External citation measurements

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Outbound references

Observation 4fac7bfb-5f1e-4da5-a50e-8f081867f79a · outbound

This paper cites Although end-to-end neural architectures have been in- creasingly adopted, classical feature-based approaches are still widely used.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Although end-to-end neural architectures have been in- creasingly adopted, classical feature-based approaches are still widely used

Reference 1

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Observation 92dcfddd-b37f-4818-aaad-6426ece01dc9 · outbound

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Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Unresolved cited work

Reference 2

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Observation bb4466be-4556-4621-9df9-a8b295813d81 · outbound

This paper cites Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis

Reference 3

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This paper cites Figure 1 presents the correlation matrix for both the SSD and HC groups after removing outliers at the 25th and 75th percentiles.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Figure 1 presents the correlation matrix for both the SSD and HC groups after removing outliers at the 25th and 75th percentiles

Reference 4

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This paper cites These results have important implications for clinical speech analysis and highlight several critical considera- tions for future research.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis These results have important implications for clinical speech analysis and highlight several critical considera- tions for future research

Reference 5

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Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Unresolved cited work

Reference 6

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Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Unresolved cited work

Reference 7

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Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Unresolved cited work

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Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Unresolved cited work

Reference 9

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Observation 4b15f1a6-efb5-48c4-8584-bcf7376bf63d · outbound

This paper cites As automated speech anal- ysis continues to gain traction in healthcare applications, en- suring reliable, interpretable, and consistent measurements be- comes imperative.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis As automated speech anal- ysis continues to gain traction in healthcare applications, en- suring reliable, interpretable, and consistent measurements be- comes imperative

Reference 10

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Observation 9faa68b0-99d5-42d0-9370-2cfae549108b · outbound

This paper cites Selection of acoustic features for speaker identifica- tion,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Selection of acoustic features for speaker identifica- tion,

Reference 11

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Observation 2a212db1-48d6-40da-98e8-29df8e26fabd · outbound

This paper cites Acoustic feature shuffling network for text-independent speaker verifica- tion.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Acoustic feature shuffling network for text-independent speaker verifica- tion

Reference 12

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Observation fcb796c2-75a9-473a-bc86-e668f4a2580d · outbound

This paper cites Acoustic feature selection for automatic emotion recognition from speech,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Acoustic feature selection for automatic emotion recognition from speech,

Reference 13

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This paper cites Survey on bimodal speech emotion recognition from acoustic and linguistic information fu- sion,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Survey on bimodal speech emotion recognition from acoustic and linguistic information fu- sion,

Reference 14

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Observation bea3ac8b-e194-4986-80e9-e4f9f4789d5d · outbound

This paper cites Selection and combination of acoustic features for the description of pathologic voices,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Selection and combination of acoustic features for the description of pathologic voices,

Reference 15

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Observation bf15a501-1ed2-4245-ae76-b850f066b391 · outbound

This paper cites Machine learning approach for clas- sification of parkinson disease using acoustic features,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Machine learning approach for clas- sification of parkinson disease using acoustic features,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:06.695532Z

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Observation 52975a7e-2a67-4b90-85fa-732cefbcc74a · outbound

This paper cites Multi- modal voice pathology detection architecture based on deep and handcrafted feature fusion,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Multi- modal voice pathology detection architecture based on deep and handcrafted feature fusion,

Reference 17

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Observation 588bc506-b246-4d44-a476-14d55447eb19 · outbound

This paper cites Acoustic feature extraction with interpretable deep neural network for neu- rodegenerative related disorder classification,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Acoustic feature extraction with interpretable deep neural network for neu- rodegenerative related disorder classification,

Reference 18

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This paper cites Opensmile: the mu- nich versatile and fast open-source audio feature extractor,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Opensmile: the mu- nich versatile and fast open-source audio feature extractor,

Reference 19

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Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Speak and unspeak with praat,

Reference 20

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Observation 143f34bf-e297-4628-a5c1-b49d71e9f769 · outbound

This paper cites Introducing parsel- mouth: A python interface to praat,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Introducing parsel- mouth: A python interface to praat,

Reference 21

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This paper cites librosa: Audio and music signal analysis in python.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis librosa: Audio and music signal analysis in python

Reference 22

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Observation 301684cf-8531-4238-a3be-d95a0f29ebc1 · outbound

This paper cites Techniques for Feature Extraction In Speech Recognition System : A Comparative Study.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Techniques for Feature Extraction In Speech Recognition System : A Comparative Study

Reference 23

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Observation 580d2015-9b7d-46ef-8838-73cff6412476 · outbound

This paper cites A comparative study of feature extraction tech- niques for speech recognition system,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis A comparative study of feature extraction tech- niques for speech recognition system,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:05.683517Z

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Observation 098854a6-e310-44af-8a57-606800de333f · outbound

This paper cites Surfboard: Audio Feature Extraction for Modern Machine Learning.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Surfboard: Audio Feature Extraction for Modern Machine Learning

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:53:59.567262Z

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Observation 7cd299e6-c970-43eb-a932-a0d4d3daf6a1 · outbound

This paper cites Speech acoustic (spac): A novel tool for speech feature extraction and classification,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Speech acoustic (spac): A novel tool for speech feature extraction and classification,

Reference 26

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Observation 7c397c82-4867-4d2e-a3b9-6ebb44eb2ace · outbound

This paper cites Repeatability of commonly used speech and lan- guage features for clinical applications,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Repeatability of commonly used speech and lan- guage features for clinical applications,

Reference 27

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Observation 45b7c6cf-bb56-4ecc-905c-a4136e6a8927 · outbound

This paper cites The reproducibility of bio- acoustic features is associated with sample duration, speech task, and gender,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis The reproducibility of bio- acoustic features is associated with sample duration, speech task, and gender,

Reference 28

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This paper cites Deep audio em- beddings for vocalisation clustering,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Deep audio em- beddings for vocalisation clustering,

Reference 29

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This paper cites Com- bining deep embeddings of acoustic and articulatory features for speaker identification,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Com- bining deep embeddings of acoustic and articulatory features for speaker identification,

Reference 30

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Observation baaafdcb-e61c-42d5-be6f-bc1937c814c2 · outbound

This paper cites The structured clinical interview for DSM-IV axis I disorders (SCID-I) and the structured clinical in- terview for DSM-IV axis II disorders (SCID-II),.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis The structured clinical interview for DSM-IV axis I disorders (SCID-I) and the structured clinical in- terview for DSM-IV axis II disorders (SCID-II),

Reference 31

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Observation d3aff093-bfc1-472d-b790-66d43a7a9f26 · outbound

This paper cites Automatic speech emotion recognition: a systematic literature review,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Automatic speech emotion recognition: a systematic literature review,

Reference 32

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Observation 21dc7b6e-505d-4255-aeee-6489e80e98e0 · outbound

This paper cites Identification of digital voice biomarkers for cognitive health,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Identification of digital voice biomarkers for cognitive health,

Reference 33

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Observation 5668bdcc-9b9e-4ee3-9164-0f24440d62f9 · outbound

This paper cites Paralin- guistic cues in speech to adapt robot behavior in human-robot in- teraction,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Paralin- guistic cues in speech to adapt robot behavior in human-robot in- teraction,

Reference 34

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Observation 56771cad-a6c0-48e3-93b4-4f517bcfcba7 · outbound

This paper cites The geneva minimalistic acoustic parameter set (gemaps) for voice research and affective computing,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis The geneva minimalistic acoustic parameter set (gemaps) for voice research and affective computing,

Reference 35

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This paper cites Towards auto- matic assessment of voice disorders: A clinical approach.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Towards auto- matic assessment of voice disorders: A clinical approach

Reference 36

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Observation b929feb3-8ece-4619-a83e-f6d9266b2ca0 · outbound

This paper cites Practical acoustics in clinical voice assessment: a praat primer,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Practical acoustics in clinical voice assessment: a praat primer,

Reference 37

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Observation 843f0146-b643-4372-930e-81aa86128ecd · outbound

This paper cites Using praat for linguistic research,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Using praat for linguistic research,

Reference 38

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Observation 3418e325-5a2c-437c-bb1b-5a30cc65e2d8 · outbound

This paper cites Deep learning approach for sound sig- nal processing,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Deep learning approach for sound sig- nal processing,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:02.702540Z

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This paper cites Evaluating the clinical utility of speech analysis and machine learning in schizophrenia: A pilot study,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Evaluating the clinical utility of speech analysis and machine learning in schizophrenia: A pilot study,

Reference 40

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Observation f0e040f5-0a3d-4d18-a3ac-de4d0c1dac01 · outbound

This paper cites Clinical classifica- tion of memory and cognitive impairment with multimodal digital biomarkers,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Clinical classifica- tion of memory and cognitive impairment with multimodal digital biomarkers,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:02.299381Z

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Observation 0ebd0fa0-d312-419b-9247-e52c860be073 · outbound

This paper cites V oice patterns as markers of schizophrenia: building a cumulative generalizable approach via a cross-linguistic and meta-analysis based investi- gation,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis V oice patterns as markers of schizophrenia: building a cumulative generalizable approach via a cross-linguistic and meta-analysis based investi- gation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:02.052076Z

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Observation d0f1e1ff-438c-4116-842e-2f02e8fee45b · outbound

This paper cites The aprosody of schizophrenia: Computationally derived acoustic phonetic underpinnings of monotone speech,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis The aprosody of schizophrenia: Computationally derived acoustic phonetic underpinnings of monotone speech,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:01.814939Z

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Observation 55d62129-8cc8-4bbc-8f41-3c37bb6d29ec · outbound

This paper cites Initial evidence of vowel space reduction in a subset of individuals with schizophrenia,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Initial evidence of vowel space reduction in a subset of individuals with schizophrenia,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:01.531637Z

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Observation e415bb42-a4e6-4e80-81ed-8a05e3941ee3 · outbound

This paper cites Digital markers of motor speech impairments in spontaneous speech of patients with als-ftd spectrum disorders,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Digital markers of motor speech impairments in spontaneous speech of patients with als-ftd spectrum disorders,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:01.303773Z

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Observation 92ac82a8-264f-4ca2-a73c-310e31eac9fb · outbound

This paper cites V ocal acoustic features may be objective biomarkers of negative symptoms in schizophrenia: A cross-sectional study,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis V ocal acoustic features may be objective biomarkers of negative symptoms in schizophrenia: A cross-sectional study,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:01.096826Z

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Observation 6b4bcf57-e73a-4269-9488-92641d5829b0 · outbound

This paper cites Algorithm for jitter and shimmer measurement in pathologic voices,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Algorithm for jitter and shimmer measurement in pathologic voices,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:00.887505Z

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Observation a68b5a43-f086-4706-a4a1-589bb47a9fb8 · outbound

This paper cites V ocal jitter and shimmer in stuttering,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis V ocal jitter and shimmer in stuttering,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:00.605204Z

Source-reported events for the cited work

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Observation 4e8ae6a6-67c6-4956-90b7-2140070d511d · outbound

This paper cites Prediction of negative symptoms of schizophrenia from emotion related low-level speech signals,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Prediction of negative symptoms of schizophrenia from emotion related low-level speech signals,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:00.397099Z

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Observation c4242994-f503-4c01-b6c4-b3927ad8ace8 · outbound

This paper cites Clinical investigation of speech signal fea- tures among patients with schizophrenia,.

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis Clinical investigation of speech signal fea- tures among patients with schizophrenia,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:00.200672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation bb4466be-4556-4621-9df9-a8b295813d81 · inbound