MétaCan
Menu
Back to cohort
Record W2305714940 · doi:10.26021/6587

The interaction between speech perception and speech production: implications for speakers with dysarthria

2013· article· en· W2305714940 on OpenAlexaboutno aff
Martina Christina Marion Schaefer

Bibliographic record

VenueUniversity of Canterbury Research Repository (University of Canterbury) · 2013
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsnot available
FundersMassachusetts Institute of Technology
KeywordsDysarthriaSpeech productionSpeech perceptionPerceptionIntelligibility (philosophy)Production (economics)PsychologyMotor theory of speech perceptionSpeech recognitionCommunicationLinguisticsComputer science

Abstract

fetched live from OpenAlex

The purpose of the research presented here was to systematically investigate the role of speech perception on speech production in speakers of different ages and those with PD and hypokinetic dysarthria. For this, the experimental designs of auditory perturbation and mimicry were chosen. The initial research phase established that the magnitude of compensation to auditory vowel perturbation was reduced in 54 speakers of New Zealand English (NZE) when compared to previous studies conducted with speakers of American (AE) and Canadian English (CE). A number of factors were studied to determine possible predictors of compensation and distinguish between potential changes associated with ageing. However, no predictors of compensation were found for the overall group. Post-hoc analyses established an increased variability in response patterns in NZE when compared to previous studies of AE and CE. Subsequent follow-up analyses focused on the response-dependent categories of (1) big compensators, (2) compensators, (3) big followers, and (4) followers. Linear mixed-effect modelling revealed that in big compensators, the magnitude of compensation was greater in speakers who exhibited larger F1 baseline standard deviation and greater F1 vowel distances of HEAD relative to HEED and HAD. F1 baseline standard deviation was found to have a similar predictive value for the group of compensators. No predictors of compensation were found for the other two subgroups. Phase two was set up as a continuation of phase one and examined whether a subset of 16 speakers classified as big compensators adapted to auditory vowel perturbation. Linear mixed-effect modelling revealed that in the absence of auditory feedback alterations, big compensators maintained their revised speech motor commands for a short period of time until a process of de-adaptation was initiated. No predictors of adaptation were found for the group. Due to the unexpected results from the first two research phases indicating a dominant weighting of somatosensory feedback in NZE compared to auditory-perceptual influences, a different experimental paradigm was selected for phase three - mimicry. The purpose of this study was to determine whether eight speakers with PD and dysarthria and eight age-matched healthy controls (HC) are able to effectively integrate speech perception and speech production when attempting to match an acoustic target. Results revealed that all speakers were able to modify their speech production to approximate the model speaker but the acoustic dimensions of their speech did not move significantly closer to the target over the three mimicry attempts. Although speakers with moderate levels of dysarthria exhibited greater acoustic distances (except for the dimension of pitch variation), neither the perceptual nor the acoustic analyses found significant differences in mimicry behaviour across the two groups. Overall, these findings were considered preliminary evidence that speech perception and speech production can at least to some extent be effectively integrated to induce error-correction mechanisms and subsequent speech motor learning in these speakers with PD and dysarthria.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.774

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 studies0.0010.001
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.285
Teacher spread0.250 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueUniversity of Canterbury Research Repository (University of Canterbury)Same topicVoice and Speech DisordersFrench-language works237,207