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A Convolutional Neural Network Model Implementation for Speech Recognition

Yıl 2019, Cilt: 7 Sayı: 3, 1892 - 1898, 31.07.2019

Şafak Kayıkçı

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

Öz

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.

Anahtar Kelimeler

Speech Recognition Deep Learning Confusion Matrix

Kaynakça

  • [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

Yıl 2019, Cilt: 7 Sayı: 3, 1892 - 1898, 31.07.2019

Şafak Kayıkçı

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

Öz

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.

Anahtar Kelimeler

Konuşma Tanıma Derin Öğrenme Karışıklık Matrisi

Kaynakça

  • [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
Toplam 9 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Şafak Kayıkçı 0000-0002-3325-4731

Yayımlanma Tarihi 31 Temmuz 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 7 Sayı: 3

Kaynak Göster

APA Kayıkçı, Ş. (2019). A Convolutional Neural Network Model Implementation for Speech Recognition. Duzce University Journal of Science and Technology, 7(3), 1892-1898. https://doi.org/10.29130/dubited.567828
AMA Kayıkçı Ş. A Convolutional Neural Network Model Implementation for Speech Recognition. DÜBİTED. Temmuz 2019;7(3):1892-1898. doi:10.29130/dubited.567828
Chicago Kayıkçı, Şafak. “A Convolutional Neural Network Model Implementation for Speech Recognition”. Duzce University Journal of Science and Technology 7, sy. 3 (Temmuz 2019): 1892-98. https://doi.org/10.29130/dubited.567828.
EndNote Kayıkçı Ş (01 Temmuz 2019) A Convolutional Neural Network Model Implementation for Speech Recognition. Duzce University Journal of Science and Technology 7 3 1892–1898.
IEEE Ş. Kayıkçı, “A Convolutional Neural Network Model Implementation for Speech Recognition”, DÜBİTED, c. 7, sy. 3, ss. 1892–1898, 2019, doi: 10.29130/dubited.567828.
ISNAD Kayıkçı, Şafak. “A Convolutional Neural Network Model Implementation for Speech Recognition”. Duzce University Journal of Science and Technology 7/3 (Temmuz 2019), 1892-1898. https://doi.org/10.29130/dubited.567828.
JAMA Kayıkçı Ş. A Convolutional Neural Network Model Implementation for Speech Recognition. DÜBİTED. 2019;7:1892–1898.
MLA Kayıkçı, Şafak. “A Convolutional Neural Network Model Implementation for Speech Recognition”. Duzce University Journal of Science and Technology, c. 7, sy. 3, 2019, ss. 1892-8, doi:10.29130/dubited.567828.
Vancouver Kayıkçı Ş. A Convolutional Neural Network Model Implementation for Speech Recognition. DÜBİTED. 2019;7(3):1892-8.

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