A Convolutional Neural Network Model Implementation for Speech Recognition
Abstract
Speech recognition is the capability of an appliance to analyze vocable and diction in a phonetic language and turn them into a machine comprehensible arrangement. It is an interdisciplinary subfield of linguistics, computer science and electrical engineering that establishes processes and techniques that understands and converts speech to text. This paper presents a convolutional neural network model for recognition of speech data.
Keywords
References
- [1] K. Davis , R. Biddulph, and S. Balashek “Automatic Recognition of Spoken Digits”, The Journal of the Acoustical Society of America, vol. 24, no. 6 , pp. 637-642, 1952.
- [2] S. Das, M. A. Picheny, In Automatic Speech and Speaker Recognition, Boston, USA: Springer, 1996, pp. 457-479
- [3] S. Hochreiter, J. Schmidhuber, “Long short-term memory”, Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997
- [4] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean and M. Kudlur “Tensorflow: A System for large-scale machine learning”, 12th Symposium on Operating Systems Design and Implementation (OSDI), Savannah, GA, USA, 2016, pp. 265-283 [5] Tensowflow Speech Commands Data Set v0.01 (2019, 01 April). [Online]. Erişim: https://www.kaggle.com/c/tensorflow-speech-recognition-challenge/data
- [6] H. Nyquist, “Certain topics in telegraph transmission theory”, Transactions of the American Institute of Electrical Engineers, vol. 47, no. 2, pp. 617-644, 1928
- [7] Davis, Steven, and P. Mermelstein, “Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences”, IEEE transactions on acoustics, speech, and signal processing, vol. 28, no. 4, pp. 357-366, 1980
- [8] Slaney, Malcolm, Michele Covell, and B. Lassiter, “Automatic audio morphing”, International Conference on Acoustics, Speech, and Signal Processing Conference (IEEE), 1996, pp. 1001-1004
- [9] S. Postalcioglu, “Performance Analysis of Different Optimizers for Deep Learning-Based Image Recognition”, International Journal of Pattern Recognition and Artificial Intelligence, 2019
Details
Primary Language
English
Subjects
Engineering
Journal Section
Review
Authors
Şafak Kayıkçı
*
0000-0002-3325-4731
Türkiye
Publication Date
July 31, 2019
Submission Date
May 20, 2019
Acceptance Date
July 6, 2019
Published in Issue
Year 2019 Volume: 7 Number: 3
Cited By
Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach
Sakarya University Journal of Computer and Information Sciences
https://doi.org/10.35377/saucis.04.01.833026