Abstract
In recent days, deep learning has been widely used in signal and information processing. Among the deep learning algorithms, Convolution Neural Network (CNN) has been widely used for image recognition and classification because of its architecture, high accuracy and efficiency. This paper proposes a method that uses the CNN on audio samples rather than on the image samples in which the CNN method is usually used to train the model. The one-dimensional audio samples are converted into two-dimensional data that consists of matrix of Mel-Frequency Cepstral Coefficients (MFCCs) that are extracted from the audio samples and the number of windows used in the extraction. This proposed CNN model has been evaluated on the TIDIGITS corpus dataset. The paper analyzes different convolution layer architectures with different number of feature maps in each architecture. The three-layer convolution architecture was found to have the highest accuracy of 97.46% among the other discussed architectures.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)
Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2013)
Kim, Y.: Convolutional neural networks for sentence classification(2014). arXiv preprint arXiv:1408.5882
Sainath, T.N., Parada, C.: Convolutional neural networks for small-footprint keyword spotting. In: Sixteenth Annual Conference of the International Speech Communication Association (2015)
Palaz, D., Magimai-Doss, M., Collobert, R.: Analysis of cnn-based speech recognition system using raw speech as input. In: Sixteenth Annual Conference of the International Speech Communication Association (2015)
Zhao, T., Zhao, Y., Chen, X.:. Time-frequency kernel-based CNN for speech recognition. In: Sixteenth Annual Conference of the International Speech Communication Association (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)
Tolias, G., Sicre, R., Jégou, H: Particular object retrieval with integral max-pooling of CNN activations (2015). arXiv preprint arXiv:1511.05879
Li, S., et al.: Shape driven kernel adaptation in convolutional neural network for robust facial traits recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Leonard, G., Doddington, G.: TIDIGITS LDC93S10. Web Download. Linguistic Data Consortium, Philadelphia (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Haque, M.A., Verma, A., Alex, J.S.R., Venkatesan, N. (2020). Experimental Evaluation of CNN Architecture for Speech Recognition. In: Luhach, A., Kosa, J., Poonia, R., Gao, XZ., Singh, D. (eds) First International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-15-0029-9_40
Download citation
DOI: https://doi.org/10.1007/978-981-15-0029-9_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0028-2
Online ISBN: 978-981-15-0029-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)