Update README.md

README.md

@@ -26,49 +26,52 @@ task_categories:
 
 ### Dataset Summary
 
-[More Information Needed]
+The SEIR-DB is a comprehensive, multilingual speech emotion intensity recognition dataset containing over 600,000 instances from various sources. It is designed to support tasks related to speech emotion recognition and emotion intensity estimation. The database includes languages such as English, Russian, Mandarin, Greek, Italian, and French.
 
 ### Supported Tasks and Leaderboards
 
-The SEIR dataset supports speech emotion recognition, and speech emotion intensity (a subset of the dataset) tasks.
+The SEIR dataset is suitable for speech emotion recognition and speech emotion intensity estimation tasks (a subset of the dataset).
 
 ### Languages
 
-SEIR-DB contains multilingual data. With languages such as English, Russian, Mandarin, Greek, Italian and French. 
+SEIR-DB encompasses multilingual data, featuring languages such as English, Russian, Mandarin, Greek, Italian, and French.
 
 ## Dataset Structure
 
 ### Data Instances
 
-The raw data collection has over 600,000 data instances (375 hours). Users of the database have access to the raw audio data, contained in subdirectories of the data directory (in their respective dataset).
+The raw data collection comprises over 600,000 data instances (375 hours). Users of the database can access the raw audio data, which is stored in subdirectories of the data directory (in their respective datasets).
 
-After processing, cleaning and formatting the dataset, we are left with roughly 120,000 training instances with an average of 3.8s length audio utterances.
+After processing, cleaning, and formatting, the dataset contains approximately 120,000 training instances with an average audio utterance length of 3.8 seconds.
 
 ### Data Fields
 
-- ID (unique sample identifier)
-- WAV (path to audio file, located in the data directory)
-- EMOTION (annotated emotion)
-- INTENSITY (annotated intensity [1-5], 1 corresponds to low intensity and 5 corresponds to high intensity, 0 corresponds to no annotation)
-- LENGTH 
+- 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 split into train, test, validation sets (located in the corresponding json manifest files).
-- Train [80%]
-- Validation [10%]
-- Test [10%]
+The data is divided into train, test, and validation sets, located in the respective JSON manifest files.
 
-The unsplit data is also available in data.csv, in order to add flexibility with custom splits. 
+- Train: 80%
+- Validation: 10%
+- Test: 10%
+
+For added flexibility, unsplit data is also available in data.csv to allow custom splits.
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-The rational behind the curation of this dataset was centered around data instance volume maximization. A large limitation with SER experimentation is the lack of emotion data, and the small size of the available datasets. This database aims to solve this problem, and give access to large amounts of emotion annotated data formatted cleanly for experimentation.
+The SEIR-DB was curated to maximize the volume of data instances, addressing a significant limitation in speech emotion recognition (SER) experimentation—the lack of emotion data and the small size of available datasets. This database aims to resolve these issues by providing a large volume of emotion-annotated data that is cleanly formatted for experimentation.
 
 ### Source Data
 
+The dataset was compiled from various sources:
+
 | Name               | Source                                                |
 |--------------------|-------------------------------------------------------|
 | AESDD (1 hr)        | [Link](http://m3c.web.auth.gr/research/aesdd-speech-emotion-recognition/) |
@@ -89,29 +92,29 @@ The rational behind the curation of this dataset was centered around data instan
 
 #### Annotation process
 
-Please refer to the source for each dataset, as they were conducted differently. However, the database is completely human annotated.
+For details on the annotation process, please refer to the source for each dataset, as they were conducted differently. However, the entire database is human-annotated.
 
 #### Who are the annotators?
 
-Please refer to the source documentation.
+Please consult the source documentation for information on the annotators.
 
 ### Personal and Sensitive Information
 
-The removal of personal and sensitive information was not attempted, as the consent and recordings were not conducted internally. 
+No attempt was made to remove personal and sensitive information, as consent and recordings were not obtained internally.
 
 ## Considerations for Using the Data
 
 ### Social Impact of Dataset
 
-[More Information Needed]
+The SEIR-DB dataset can significantly impact the research and development of speech emotion recognition technologies by providing a large volume of annotated data. These technologies have the potential to enhance various applications, such as mental health monitoring, virtual assistants, customer support, and communication devices for people with disabilities.
 
 ### Discussion of Biases
 
-[More Information Needed]
+During the dataset cleaning process, efforts were made to balance the database concerning the number of samples for each dataset, emotion distribution (with a greater focus on primary emotions and less on secondary emotions), and language distribution. However, biases may still be present.
 
 ### Other Known Limitations
 
-[More Information Needed]
+No specific limitations have been identified at this time.
 
 ## Additional Information