Evaluation of an Arabic Speech Corpus of Emotions: A Perceptual and Statistical Analysis
Bibliographic record
Abstract
The processing of emotion has a wide range of applications in many different fields and has become the subject of increasing interest and attention for many speech and language researchers. Speech emotion recognition systems face many challenges. One of these is the degree of naturalness of the emotions in speech corpora. To prove the ability of speakers to accurately emulate emotions and to check whether listeners could identify the intended emotion, a human perception test was designed for a new emotional speech corpus. This paper presents an exhaustive statistical and perceptual investigation of the emotional speech corpus (KSUEmotions) for Arabic King Saud University approved by the Linguistic Data Consortium. The KSUEmotions corpus was built in two phases and involved 23 native speakers (10 males and 13 females) to emulate the following five emotions: neutral, sadness, happiness, surprise, and anger. Nine listeners were participated in a blind and randomly structured human perceptual test to assess the validity of the intended emotions. Statistical tests were used to analyze the effects of speaker gender, reviewer (listener) gender, emotion type, sentence length, and the interaction between these factors. Conducted statistical tests included the two-way analysis of variance, normality, chi-square, Bonferroni, Tukey, and Mann–Whitney U tests. One of the outcomes of the study is that the speaker gender, emotion type, and interaction between emotion type and speaker gender yield significant effects on the emotion perception in this corpus.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".