Automatic identification of filled pauses in spontaneous speech
Bibliographic record
Abstract
Practical speech recognizers must accept normal conversational voice input (including hesitations). However, most automatic speech recognition work has concentrated on read speech, whose acoustic aspects differ significantly from speech found in actual dialogues. Hesitations, of which the most frequent are filled pauses, are common in natural speech, yet few recognition systems handle such disfluencies with any degree of success. Filled pauses (e.g., "uhh", "umm"), unlike most silent pauses, resemble phones which form words in continuous speech. The work reported here further develops techniques to allow automatic identification of filled pauses. Such identification, if reliable, would reduce potential confusion in determining an estimated textual output for an utterance. The Switchboard database (of natural telephone conversations) provided data for the study. While most automatic recognition methods rely entirely on spectral envelope (e.g., low-order cepstral coefficients), identifying filled pauses requires using a combination of spectra, fundamental frequency and duration. High precision and a low false alarm rate for filled pauses are feasible without excessive computation.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".