Recognizing Speakers' Geographical Divisions from Continuous Bengali Speech using Artificial Neural Networks

Belangrijkste concepten

A deep learning model using Artificial Neural Networks can effectively recognize the geographical division of Bangladeshi speakers from their continuous Bengali speech.

Samenvatting

The researchers developed a method to recognize the geographical division of Bangladeshi speakers from their continuous Bengali speech using Artificial Neural Networks. They collected over 45 hours of audio data from 633 speakers across 8 divisions of Bangladesh and performed preprocessing tasks like noise reduction and audio segmentation.

The key highlights of the study are:

• They extracted Mel Frequency Cepstral Coefficients (MFCC) and delta features from the speech data as input to the neural network model.
• The proposed Artificial Neural Network model had 5 dense layers with ReLU activation, dropout regularization, and a softmax output layer for 8-way division classification.
• The model was trained using the Adam optimizer and categorical cross-entropy loss, achieving a highest accuracy of 85.44% on the validation set.
• The researchers analyzed the model's performance using a confusion matrix, which showed strong classification across the 8 Bangladeshi divisions.

The authors conclude that their deep learning approach can effectively recognize the geographical division of Bangladeshi speakers from their continuous Bengali speech, which has applications in areas like speaker identification, crime investigation, and fraud detection.

Statistieken

The dataset contained over 45 hours of audio data from 633 speakers (416 male, 217 female) across 8 divisions of Bangladesh.
Each audio sample was segmented into 8-10 second chunks before feature extraction.

Citaten

"Speech is one of the easiest mediums of communication because it has a lot of identical features for different speakers."
"Accent-based speaker recognition is one of the emerging topics for ASR researchers."
"Deep learning algorithms like RNN, ANN, CNN, and LSTM are performing better for speech recognition because of their perfectly structured data."

Belangrijkste Inzichten Gedestilleerd Uit

Artificial Neural Networks to Recognize Speakers Division from Continuous Bengali Speech

by Hasmot Ali,M... om arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.15168.pdf

Diepere vragen

How can the proposed model be extended to recognize speakers' accents or dialects within each division?

To extend the proposed model to recognize speakers' accents or dialects within each division, additional features and training data specific to accents or dialects need to be incorporated. One approach could be to include accent-specific phonetic features in addition to the existing MFCC and delta features. By training the model on a dataset that includes variations in accents within each division, the model can learn to differentiate between different accents or dialects. Utilizing accent-specific datasets and incorporating accent-related features during the training process can enhance the model's ability to recognize and classify speakers based on their accents within each division.

What other speech features or deep learning architectures could be explored to further improve the division recognition accuracy?

In addition to MFCC and delta features, other speech features such as pitch, formants, prosody, and spectral features could be explored to further improve the division recognition accuracy. These features can provide additional information about the speaker's characteristics and speech patterns, which can help in distinguishing between speakers from different divisions more effectively.
Regarding deep learning architectures, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) could be investigated for their potential in enhancing division recognition accuracy. CNNs can capture spatial dependencies in the input data, while RNNs are well-suited for sequential data like speech signals. By experimenting with different architectures and combinations of features, the model's performance in recognizing speakers' divisions can be optimized.

How could this speaker division recognition system be integrated with other speech-based applications like speaker identification or fraud detection?

The speaker division recognition system can be integrated with other speech-based applications like speaker identification or fraud detection by leveraging the identified divisions as additional attributes for speaker profiling. By combining the division recognition output with existing speaker identification systems, the overall accuracy and reliability of speaker identification can be enhanced.
In fraud detection scenarios, the recognized speaker divisions can serve as valuable metadata for identifying potential fraudulent activities originating from specific regions. By incorporating the division recognition system into the fraud detection pipeline, suspicious activities associated with certain divisions can be flagged for further investigation. This integration can strengthen the fraud detection process and improve the overall security of voice-based applications.