GreThE

A Dataset for Speech Emotion Recognition in Greek Theatrical Plays

1. General Info

Info
# samples	500
Total duration	46 mins
# Classification tasks	2 (valence and arousal)
# human annotators	4
# theatrical plays used	23
# unique speakers	90
Language	Greek

2 Dataset format

2.1 Classification Tasks

The Arousal and Valence tasks are provided in a classification format under three classes: (i) weak, (ii) neutral, (iii) strong for arousal and (i) negative, (ii) neutral, (iii) positive for valence.

2.2 Available features

Three types of numpy binaries (npy files) are provided for each audio sample:

1. Mel-spectrograms
2. a sequence of 68 segment feature vectors calculated by pyAudioAnalysis, using a 50 msec non overlapping window. In other words, each utterance is represented by a number_of_frames x 68 short-term features.
3. a sequence of segment statistics (i.e. the mean and std of the 68 short-term fetures, that is a 136-D feature vector for the whole utterance) calculated by pyAudioAnalysis.

2.3 Filenames and metadata

The filenames of the examples are in the form of <id1>_speaker<id2>-<id3>.npy where:

• id1 is the session (ie. theatrical play) id
• id2 is the speaker id
• id3 is the utterance id for a specific speaker and session

This information can be used for session depedent cross-validation.

3. Feature extraction

In order to perform the same feature extraction procedure on new (unseen) raw audio files, you can use the get_melgram and pyaudio_segment_features functions found in feature_extraction.py

Note that the raw audio files must be mono and have a sampling rate of 8K.

4. Basic Evaluation

The evaluation.py srcipt is provided in order to perform a basic session-independent evaluation using feature statistics and pyAudioAnalysis.

First install dependencies by:

pip3 install -r requirements.txt

For arousal, run:

python3 evaluation.py -p data/arousal/pyaudio/segment_stats/weak data/arousal/pyaudio/segment_stats/neutral data/arousal/pyaudio/segment_stats/strong

For valence, run:

python3 evaluation.py -p data/valence/pyaudio/segment_stats/negative data/valence/pyaudio/segment_stats/neutral data/valence/pyaudio/segment_stats/positive

5. Cite

To be filled