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Record W2167039028 · doi:10.3766/jaaa.18.7.4

How Competing Speech Interferes with Speech Comprehension in Everyday Listening Situations

2007· article· en· W2167039028 on OpenAlexaff
Bruce A. Schneider, Liang Li, Meredyth Daneman

Bibliographic record

VenueJournal of the American Academy of Audiology · 2007
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMasking (illustration)Speech recognitionComprehensionActive listeningConversationComputer scienceNoise (video)Speech perceptionPsychologyCommunicationPerceptionArtificial intelligence

Abstract

fetched live from OpenAlex

Listeners often complain that they have trouble following a conversation when the environment is noisy. The environment could be noisy because of the presence of other unrelated but meaningful conversations, or because of the presence of less meaningful sound sources such as ventilation noise. Both kinds of distracting sound sources produce interference at the auditory periphery (activate similar regions along the basilar membrane), and this kind of interference is called "energetic masking." However, in addition to energetic masking, meaningful sound sources, such as competing speech, can and do interfere with the processing of the target speech at more central levels (phonetic and/or semantic), and this kind of interference is often called informational masking. In this article we review what is known about informational masking of speech by competing speech, and the auditory and cognitive factors that determine its severity. Las personas que escuchan a menudo tienen problema para seguir una conversación cuando el ambiente es ruidoso. El ambiente puede ser ruidoso por la presencia de otras conversaciones no relacionadas pero significativas, o por la presencia de otras fuentes de ruido menos significativas, tales como ruidos de ventilación. Ambas fuentes de sonidos de distracción producen interferencia en la periferia auditiva (activan regiones similares a lo largo de la membrana basilar), y este tipo de interferencia se la llama enmascaramiento energético. Sin embargo, además del enmascaramiento energético, otras fuentes significativas de sonido, tales como el lenguaje en competencia, pueden interferir, y de hecho interfieren con el procesamiento de un lenguaje meta a niveles más centrales (fonético y/o semántico), y este tipo de interferencia se le llama enmascaramiento informacional. En este artículo revisamos lo que se conoce sobre enmascaramiento informacional del lenguaje por lenguaje competitivo, y los factores auditivos y cognitivos que determinan su severidad.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.319
Teacher spread0.285 · 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

Citations149
Published2007
Admission routes1
Has abstractyes

Explore more

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