A human vocal utterance corpus for perceptual and acoustic analysis of speech, singing, and intermediate vocalizations
Bibliographic record
Abstract
In this paper we present the collection and annotation process of a corpus of human utterance vocalizations used for speech and song research. The corpus was collected to fill a void in current research tools, since no corpus currently exists which is useful for the classification of intermediate utterances between speech and monophonic singing. Much work has been done in the domain of speech versus music discrimination, and several corpora exist which can be used for this research. A specific example is the work done by Eric Scheirer and Malcom Slaney [IEEE ICASSP, 1997, pp. 1331–1334]. The collection of the corpus is described including questionnaire design and intended and actual response characteristics, as well as the collection and annotation of pre-existing samples. The annotation of the corpus consisted of a survey tool for a subset of the corpus samples, including ratings of the clips based on a speech–song continuum, and questions on the perceptual qualities of speech and song, both generally and corresponding to particular clips in the corpus.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".