Recognition of syllables in a continuous stream of speech by parcor parameters of linear predictive vocoder
Bibliographic record
Abstract
Linear predictive coding (LPC) has been used to compress and encode speech signals for digital transmission at a low bit rate. PARCOR parameter associated with LPC that represents a vocal tract model based on a lattice filter structure is considered for speech recognition. The use of FIR coefficients and the frequency response of AR model were previously investigated. This paper reports a method to detect syllables from a continuous stream of speech. The system being developed slides a time window of 20 ms and calculates the PARCOR parameters continuously, feeding them to a syllable classifier. The syllable classifier is a supervised classifier that requires training. The training uses TIMIT speech database, which contains the recordings of 630 speakers of 8 major dialects of American English. The voiced/unvoiced switch built into the LPC vocoder was modified to segment words included in the speech records. Preliminary results of classification are presented in the paper.
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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 | 0.000 | 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) | 0.000 | 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".