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.
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".