Segregation of voices with single or double fundamental frequencies
Bibliographic record
Abstract
In cocktail-party situations, listeners can use the fundamental frequency (F0) of a voice to segregate it from competitors, but other cues in speech could help, such as co-modulation of envelopes across frequency or more complex cues related to the semantic/syntactic content of the utterances. For simplicity, this (non-pitch) form of grouping is referred to as "articulatory." By creating a new type of speech with two steady F0s, it was examined how these two forms of segregation compete: articulatory grouping would bind the partials of a double-F0 source together, whereas harmonic segregation would tend to split them in two subsets. In experiment 1, maskers were two same-male sentences. Speech reception thresholds were high in this task (vicinity of 0 dB), and harmonic segregation behaved as though double-F0 stimuli were two independent sources. This was not the case in experiment 2, where maskers were speech-shaped complexes (buzzes). First, double-F0 targets were immune to the masking of a single-F0 buzz matching one of the two target F0s. Second, double-F0 buzzes were particularly effective at masking a single-F0 target matching one of the two buzz F0s. As a conclusion, the strength of F0-segregation appears to depend on whether the masker is speech or not.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".