A feature selection based parallelized CNN-BiGRU network for speech emotion recognition in Odia language
Bibliographic record
Abstract
Abstract Emotion recognition from speech is an integral part of human interaction. This paper represents work that includes the creation and evaluation of speech emotion recognition on the Odia database. Previous research in this field implemented several benchmark datasets. In this work, we use two benchmark datasets for cross-validation with our own created Odia dataset name as SITB-OSED. Initially, the spectral, prosodic, and voice quality features are extracted from a raw audio file; secondly, a Gradient Boost Decision Tree (GBDT) feature selection method is used to remove all the redundant features and select the potential features. Here, two distinct series of experiments are performed. Firstly, the baseline model, which takes all the combined selected features, is chosen as input (spectral, prosodic, and voice quality features). Secondly, the proposed model processes all the selected features through two separate channels, the Convolutional neural network (CNN) and Bi-directional gated recurrent units (Bi-GRU). Specifically, the proposed method achieved 6.67%, 6.03%, and 5.55% higher accuracy than the baseline model on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) Interactive Emotional Dyadic Motion Capture (IEMOCAP) and SITB-OSED datasets. We also report that the proposed parallelized CNN-BiGRU model outperforms the recent state-of-the-art methods with an accuracy of 82.29% and 78.54% on the RAVDESS and IEMOCAP datasets, respectively. For our SITB-OSED dataset, the overall recognition accuracy of 84.02% is achieved.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".