Estimation of the Underlying F0 Range of a Speaker from the Spectral Features of a Brief Speech Input
Bibliographic record
Abstract
From a very brief speech, human listeners can estimate the pitch range of the speaker and normalize pitch perception. Spectral features which inherently involve both articulatory and phonatory characteristics were speculated to play roles in this process, but few were reported to directly correlate with speaker’s F0 range. To mimic this human auditory capability and validate the speculation, in a preliminary study we proposed an LSTM-based method to estimate speaker’s F0 range from a 300 ms-long speech input, which turned out to outperform the conventional method. By two more experiments, this study further improved the method and verified its validity in estimating the speaker-specific underlying F0 range. After incorporating a novel measurement of F0 range and a multi-task training approach, Experiment 1 showed that the refined model gave more accurate estimates than the initial model. Based on a Japanese-Chinese bilingual parallel speech corpus, Experiment 2 found that the F0 ranges estimated with the model from the Chinese speech and the model from the Japanese speech produced by the same set of speakers had no significant difference, whereas the conventional method showed significant difference. The results indicate that the proposed spectrum-based method captures the speaker-specific underlying F0 range which is independent of the linguistic content.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".