Update README.md

README.md

@@ -11,17 +11,10 @@ size_categories:
 - 100K<n<1M
 task_categories:
 - audio-classification
-extra_gated_prompt: "To obtain an access token, the database licence must be purchased through https://gabegiangi.wordpress.com/2023/05/15/seir-db/"
-extra_gated_fields:
- Name: text
- Email: text
- Company: text
- Country: text
- Access Token: text
- I agree not to give access to any other entities: checkbox
+tags:
+- SER
 ---
 
-
 # Speech Emotion Intensity Recognition Database (SEIR-DB)
 
 ## Dataset Description
@@ -38,9 +31,27 @@ The SEIR-DB is a comprehensive, multilingual speech emotion intensity recognitio
 
 ### Supported Tasks and Leaderboards
 
-The SEIR dataset is suitable for speech emotion recognition and speech emotion intensity estimation tasks (a subset of the dataset).
+The SEIR-DB is suitable for:
+- **Speech Emotion Recognition** (classification of discrete emotional states)
+- **Speech Emotion Intensity Estimation** (a subset of this dataset, where intensity is rated from 1–5)
+
+#### SPEAR (8 emotions – 375 hours)
+
+[SPEAR (Speech Emotion Analysis and Recognition System)](mailto:[email protected]) is an **ensemble model** developed by Gabriel Giangi and serves as the SER **benchmark** for this dataset. Below is a comparison of its performance against the best fine-tuned pre-trained model (WavLM Large):
+
+| WavLM Large Test Accuracy | SPEAR Test Accuracy | Improvement |
+|---------------------------|---------------------|-------------|
+| 87.8%                     | 90.8%              | +3.0%       |
+
+More detailed metrics for **SPEAR**:
+
+| Train Accuracy (%) | Validation Accuracy (%) | Test Accuracy (%) |
+|--------------------|-------------------------|-------------------|
+| 99.8%                  | 90.4%             | 90.8%              |
+
+---
 
-### Languages
+## Languages
 
 SEIR-DB encompasses multilingual data, featuring languages such as English, Russian, Mandarin, Greek, Italian, and French.
 
@@ -54,21 +65,21 @@ After processing, cleaning, and formatting, the dataset contains approximately 1
 
 ### Data Fields
 
-- ID: unique sample identifier
-- WAV: path to the audio file, located in the data directory
-- EMOTION: annotated emotion
-- INTENSITY: annotated intensity (ranging from 1-5), where 1 denotes low intensity, and 5 signifies high intensity; 0 indicates no annotation
-- LENGTH: duration of the audio utterance
+- **ID**: unique sample identifier  
+- **WAV**: path to the audio file, located in the data directory  
+- **EMOTION**: annotated emotion  
+- **INTENSITY**: annotated intensity (ranging from 1-5), where 1 denotes low intensity, and 5 signifies high intensity; 0 indicates no annotation  
+- **LENGTH**: duration of the audio utterance  
 
 ### Data Splits
 
 The data is divided into train, test, and validation sets, located in the respective JSON manifest files.
 
-- Train: 80%
-- Validation: 10%
-- Test: 10%
+- **Train**: 80%  
+- **Validation**: 10%  
+- **Test**: 10%
 
-For added flexibility, unsplit data is also available in data.csv to allow custom splits.
+For added flexibility, unsplit data is also available in `data.csv` to allow custom splits.
 
 ## Dataset Creation
 
@@ -112,11 +123,11 @@ No specific limitations have been identified at this time.
 
 ### Dataset Curators
 
-Gabriel Giangi - Concordia University - Montreal, QC Canada - [email protected]
+Gabriel Giangi - Concordia University - Montreal, QC Canada - [[email protected]](mailto:[email protected])
 
 ### Licensing Information
 
-This dataset can be used for research and academic purposes. For commercial purposes, please contact [email protected] .
+This dataset can be used for research and academic purposes. For commercial purposes, please contact [[email protected]](mailto:gabegiangi@gmail.com).
 
 ### Citation Information
 
@@ -174,4 +185,4 @@ Zhang, R., & Liu, M. (2020). Speech emotion recognition with self-attention. In
 
 ### Contributions
 
-Gabriel Giangi - Concordia University - Montreal, QC Canada - [email protected]
+Gabriel Giangi - Concordia University - Montreal, QC Canada - [[email protected]](mailto:[email protected])