Multiple windowed spectral features for emotion recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
MFCC (Mel Frequency Cepstral Coefficients) and PLP (Perceptual linear prediction coefficients) or RASTA-PLP have demonstrated good results whether when they are used in combination with prosodic features as suprasegmental (long-term) information or when used stand-alone as segmental (short-time) information. MFCC and PLP feature parameterization aims to represent the speech parameters in a way similar to how sound is perceived by humans. However, MFCC and PLP are usually computed from a Hamming-windowed periodogram spectrum estimate that is characterized by large variance. In this paper we study the effect of averaging spectral estimates obtained using a set of orthogonal tapers (windows) on emotion recognition performance. The multitaper MFCC and PLP are examined separately as short-time information vectors modeled using Gaussian mixture models (GMMs). When tested on the FAU AIBO spontaneous emotion corpus, a relative improvement ranging from 2.2% to 3.9% for both MFCC and PLP systems is achieved by multiple windowed spectral features compared to single windowed ones.
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.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it