Deep learning and SVM‐based emotion recognition from Chinese speech for smart affective services
Bibliographic record
Abstract
Summary Emotion recognition is challenging for understanding people and enhances human–computer interaction experiences, which contributes to the harmonious running of smart health care and other smart services. In this paper, several kinds of speech features such as Mel frequency cepstrum coefficient, pitch, and formant were extracted and combined in different ways to reflect the relationship between feature fusions and emotion recognition performance. In addition, we explored two methods, namely, support vector machine (SVM) and deep belief networks (DBNs), to classify six emotion status: anger, fear, joy, neutral status, sadness, and surprise. In the SVM‐based method, we used SVM multi‐classification algorithm to optimize the parameters of penalty factor and kernel function. With DBN, we adjusted different parameters to achieve the best performance when solving different emotions. Both gender‐dependent and gender‐independent experiments were conducted on the Chinese Academy of Sciences emotional speech database. The mean accuracy of SVM is 84.54%, and the mean accuracy of DBN is 94.6%. The experiments show that the DBN‐based approach has good potential for practical usage, and suitable feature fusions will further improve the performance of speech emotion recognition. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".