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Record W2898909349 · doi:10.1109/embc.2018.8512618

Objective and Subjective Assessment of Amplified Parkinsonian Speech Quality

2018· article· en· W2898909349 on OpenAlexaff
Amr Gaballah, Vijay Parsa, Monika Andreetta, Scott Adams

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsWestern University
Fundersnot available
KeywordsSpeech recognitionLoudnessMel-frequency cepstrumCorrelationLinear predictive codingComputer scienceLinear regressionSupport vector machineCepstrumLinear predictionNoise (video)AudiologyRegressionQuality (philosophy)Regression analysisSet (abstract data type)Speech processingPattern recognition (psychology)Artificial intelligenceFeature extractionMathematicsStatisticsMedicineMachine learning

Abstract

fetched live from OpenAlex

Hypophonia is a common speech impairment associated with Parkinson's disease (PD). Voice amplifiers are typically used to increase voice loudness, but little is known about their impact on perceived speech quality. In this paper, speech recordings were obtained from 11 PD subjects with and without the use of seven different amplification devices, and in the absence or presence of background noise. The recorded speech samples were rated for their sound quality by 10 naive listeners. The same speech recordings were analyzed objectively, where in linear prediction, mel-frequency cepstral coefficients (MFCCs), and gammatone cepstral coefficients (GFCCs) were extracted and mapped to predicted quality scores using linear regression and Support Vector Regression (SVR). Results showed that amplification devices differentially affect the perceived quality of PD speech, that objective and subjective quality scores correlated well, and that a reduced set of GFCC features mapped with SVR produced the best correlation with the subjective scores.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.341
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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