romanyshyn-natalia/english-accents-classification

Signal Processing Course project

Accent classification for native and non-native English speakers

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.

Installation

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-classification

Requirements

The following command installs all necessary packages:

pip install -r requirements.txt

Dataset

I 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.

Experiment steps

• data exploration and preprocessing;
• MFCC features extraction;
• defining CNN model for classification;
• training and inferense;
• results analysis.

Results

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.

Contributing

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.