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Record W2515653097 · doi:10.32920/25413256

Perception of Emotional Speech by Listeners With Hearing Aids

2024· article· en· W2515653097 on OpenAlexafffund
Huiwen Goy, M. Kathleen Pichora‐Fuller, Gurjit Singh, Frank Russo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsToronto Metropolitan UniversityToronto Rehabilitation InstituteUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsPerceptionPsychologyAudiologySpeech perceptionSpeech recognitionComputer scienceMedicine

Abstract

fetched live from OpenAlex

<p>A talker’s emotional state is one important type of information carried by the speech signal. While the frequency and amplitude compression performed by hearing aids may make speech easier to understand, little is known about how such processing affects users’ perception of emotion in speech. This study investigated how hearing aid use affected the perception of emotion in speech and the recognition of speech spoken with emotion. Listeners were hearing aid users who were tested with and without their aids in separate sessions. They heard sentences spoken by a young female actor portraying different vocal emotions, and were asked to report the keyword and identify the portrayed emotion. The use of hearing aids improved listeners’ word recognition performance from 43% correct (unaided) to 68% correct (aided). In contrast, hearing aids did not improve listeners’ emotion identification (38% unaided, compared to 40% aided). Emotions that were more easily identified were not necessarily the same emotions associated with better word recognition. We conclude that the types of information carried by the speech signal are differentially affected by hearing aids; in this case, hearing aids improved the recognition of what was spoken but not the identification of vocal emotion.</p>

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.763
Threshold uncertainty score0.160

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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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 designBench or experimental
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

Citations9
Published2024
Admission routes2
Has abstractyes

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