How Competing Speech Interferes with Speech Comprehension in Everyday Listening Situations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".