A Convolutional Neural Network model based on Neutrosophy for Noisy Speech Recognition

TL;DR

This paper introduces NCNN, a novel CNN model that incorporates neutrosophic domain uncertainty modeling to improve noisy speech recognition accuracy, outperforming conventional CNNs especially in challenging noisy environments.

Contribution

The paper proposes a new CNN architecture that integrates uncertainty modeling in the neutrosophic domain for robust noisy speech recognition.

Findings

Achieves an average accuracy of 85.96% on noisy speech data.

Outperforms conventional CNN by 6%, 5%, and 2% in different noisy test sets.

Demonstrates robustness against various real-world noise conditions.

Abstract

Convolutional neural networks are sensitive to unknown noisy condition in the test phase and so their performance degrades for the noisy data classification task including noisy speech recognition. In this research, a new convolutional neural network (CNN) model with data uncertainty handling; referred as NCNN (Neutrosophic Convolutional Neural Network); is proposed for classification task. Here, speech signals are used as input data and their noise is modeled as uncertainty. In this task, using speech spectrogram, a definition of uncertainty is proposed in neutrosophic (NS) domain. Uncertainty is computed for each Time-frequency point of speech spectrogram as like a pixel. Therefore, uncertainty matrix with the same size of spectrogram is created in NS domain. In the next step, a two parallel paths CNN classification model is proposed. Speech spectrogram is used as input of the first path and uncertainty matrix for the second path. The outputs of two paths are combined to compute the final output of the classifier. To show the effectiveness of the proposed method, it has been compared with conventional CNN on the isolated words of Aurora2 dataset. The proposed method achieves the average accuracy of 85.96 in noisy train data. It is more robust against Car, Airport and Subway noises with accuracies 90, 88 and 81 in test sets A, B and C, respectively. Results show that the proposed method outperforms conventional CNN with the improvement of 6, 5 and 2 percentage in test set A, test set B and test sets C, respectively. It means that the proposed method is more robust against noisy data and handle these data effectively.