A New Technique for the Estimation of Jitter and Shimmer of Voiced Speech Signal
Bibliographic record
Abstract
This paper presents a new technique for the estimation of jitter and shimmer exploiting the detected value of the pitch-period and amplitude of voiced speech signal degraded by white noise. Perturbations of the glottis cycle duration (jitter) and intonation (shimmer) are, respectively, the irregularities of pitch-period and energy. We propose a modified circular autocorrelation function (MCACF) of the normalized hamming-windowed pre-filtered speech. A weighted and pitch-harmonic MCACF is utilized to effectively extract pitch-period for jitter computation. To carry out shimmer analysis, peak-to-peak amplitude of signal around the estimated pitch-period is optimally obtained by matching the windowed pre-filtered speech frame with a shifted impulse train. For performance evaluation vowels, such as "a, e, i, o, u", are used from the TIMIT database. The conventional algorithms manifest not only the distortion of pitch contours but also provides elevated values of the jitter and shimmer that can be interpreted as the presence of anomalies of the vocal tract, the glottis or the neuro-mechanism. On the contrary, for both clean and noisy speech, the proposed method results in a comparatively smooth pitch contour as well as small values of the jitter and shimmer confirming the presence of a normal voice with good stability and periodicity
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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".