Bibliographic record
Abstract
The research documented in this thesis presents a pitch synchronous speech analysis and synthesis system using an ARMA (auto regressive moving average) lattice filter. The LP (linear predictive) model is, by far, the most widely used speech production model in speech processing. A well-known drawback with the LP model is that it ignores the effect of the nasal cavity. The mismatch between the model and the vocal tract becomes severe for nasal sound production and leads to poor synthesized speech. The nasal tract effect can be included in the speech production model by representing the vocal tract with an ARMA filter. Thus, better synthesis speech is expected, at least for nasal phonemes. In this thesis, the ARMA lattice filter is used to model the vocal tract. Based on analysis and experiments, it is concluded that the estimation of excitation source for voiced speech is essential in obtaining the ARMA lattice filter coefficients and important in generating high quality synthesis speech. By comparison, LF model is chosen for this purpose. In the proposed analysis system, the analysis parameters include: ARMA filter coefficients, LF model parameters, voice type, pitch and gain, which are analyzed pitch synchronously. Correspondingly, the pitch synchronous speech synthesis system is developed. At the end, the proposed system is simulated for both nasal and non-nasal phonemes and the results are compared with those from the LP model.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .W36. Source: Masters Abstracts International, Volume: 42-02, page: 0652. Adviser: H. K. Kwan. Thesis (M.A.Sc.)--University of Windsor (Canada), 2003.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".