Signal Processing Course project
Briefly, the accent is the way you sound when you speak. The accent classification task identifies the accent being spoken by a person so that the correct words being spoken can be identified by further processing since the same noises can mean entirely different words in different accents of the same language.
To install from our GitHub repository, you can do the following:
git clone https://github.com/romanyshyn-natalia/english-accents-classification.git
cd english-accents-classificationThe following command installs all necessary packages:
pip install -r requirements.txtI utilized AccentDB, which has three datasets that can be downloaded from here.
| Title | Description | Notes |
|---|---|---|
| accentdb_core | 4 non-native Indian English accents collected by authors. | 6,587 files |
| accentdb_extended | Samples for 5 English Accents + 4 accents from accentdb_core. | 19,111 files |
| accentdb_raw | Raw and unprocessed recordings for the core dataset. | 11 files |
For the current research, 742 samples for speaker_1 from accentdb_extended| version was used.
- data exploration and preprocessing;
- MFCC features extraction;
- defining CNN model for classification;
- training and inferense;
- results analysis.
With split of 70:30 between training and validation sets and, after the training with five epochs, the accuracy during the inference is 98%, which is quite remarkable.
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.