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A Convolutional Neural Network Model Implementation for Speech Recognition

Year 2019, Volume: 7 Issue: 3, 1892 - 1898, 31.07.2019

Şafak Kayıkçı

https://doi.org/10.29130/dubited.567828
Cited By: 1

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

Speech Recognition Deep Learning Confusion Matrix

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
  • [10] Townsend, T. James “Theoretical analysis of an alphabetic confusion matrix”, Perception & Psychophysics, vol. 9, no. 1, pp. 40-50, 1971

Konuşma Tanıma için Bir Evrimsel Sinir Ağı Modeli Uygulaması

Year 2019, Volume: 7 Issue: 3, 1892 - 1898, 31.07.2019

Şafak Kayıkçı

https://doi.org/10.29130/dubited.567828
Cited By: 1

Abstract

Konuşma tanıma, bir
cihazın fonetik bir dilde kelime bilgisi ile diksiyonu analiz etme ve bunları
makinenin anlaşılır bir düzenine dönüştürebilme kabiliyetidir. Konuşmayı
anlayan ve metne dönüştüren süreç ve teknikleri oluşturan disiplinlerarası bir
dilbilim olup bilgisayar bilimi ve elektrik mühendisliği alt alanıdır. Bu çalışmada
konuşma verilerinin tanınması için evri bir sinir ağı modeli sunulmaktadır.

Keywords

Konuşma Tanıma Derin Öğrenme Karışıklık Matrisi

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
  • [10] Townsend, T. James “Theoretical analysis of an alphabetic confusion matrix”, Perception & Psychophysics, vol. 9, no. 1, pp. 40-50, 1971
There are 9 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Şafak Kayıkçı 0000-0002-3325-4731

Publication Date July 31, 2019
Published in Issue Year 2019 Volume: 7 Issue: 3

Cite

APA Kayıkçı, Ş. (2019). A Convolutional Neural Network Model Implementation for Speech Recognition. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, 7(3), 1892-1898. https://doi.org/10.29130/dubited.567828
AMA Kayıkçı Ş. A Convolutional Neural Network Model Implementation for Speech Recognition. DUBİTED. July 2019;7(3):1892-1898. doi:10.29130/dubited.567828
Chicago Kayıkçı, Şafak. “A Convolutional Neural Network Model Implementation for Speech Recognition”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi 7, no. 3 (July 2019): 1892-98. https://doi.org/10.29130/dubited.567828.
EndNote Kayıkçı Ş (July 1, 2019) A Convolutional Neural Network Model Implementation for Speech Recognition. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 7 3 1892–1898.
IEEE Ş. Kayıkçı, “A Convolutional Neural Network Model Implementation for Speech Recognition”, DUBİTED, vol. 7, no. 3, pp. 1892–1898, 2019, doi: 10.29130/dubited.567828.
ISNAD Kayıkçı, Şafak. “A Convolutional Neural Network Model Implementation for Speech Recognition”. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 7/3 (July 2019), 1892-1898. https://doi.org/10.29130/dubited.567828.
JAMA Kayıkçı Ş. A Convolutional Neural Network Model Implementation for Speech Recognition. DUBİTED. 2019;7:1892–1898.
MLA Kayıkçı, Şafak. “A Convolutional Neural Network Model Implementation for Speech Recognition”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, vol. 7, no. 3, 2019, pp. 1892-8, doi:10.29130/dubited.567828.
Vancouver Kayıkçı Ş. A Convolutional Neural Network Model Implementation for Speech Recognition. DUBİTED. 2019;7(3):1892-8.

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