TY  - JOUR
T1  - Turkish Speech recognition using Mel-frequency cepstral coefficients(MFCC) and Hidden Markov Model (HMM)
TT  - Mel-Frekans Kepstral Katsayılar ve Gizli Markov Model Kullanılarak Türkçe Konuşma Tanıma
AU  - Kocer, Hasan Erdinc
AU  - Ahmed, Mustafa Cumaah
PY  - 2019
DA  - December
JF  - Veri Bilimi
JO  - Data Sci. J.
PB  - Murat GÖK
WT  - DergiPark
SN  - 2667-582X
SP  - 39
EP  - 44
VL  - 2
IS  - 2
LA  - en
AB  - In this paper, a new Turkishspoken number recognition system proposed. The Mel-frequency cepstralcoefficients (MFCC) algorithm used as a feature extraction method, the GaussianHidden Markov model, used for numbers phonemes modeling where each number has aMarkov model. The system trained on a dataset collected from 20 subjects thatincludes 7 females and 13 males. Each one says the Turkish numbers from “zero”to “ten”. Audio files sampled at 8000Hz at each second and each file hasone-second length and recorded in an isolated environment. We tested the systemusing random records for different people. The training files include 220 audiorecord and testing files include 18 audio record. The system achieves %83.3accuracy, %86 precision, and %83 recall rates.
KW  - Hidden Markov Model
KW  - Mel-Frequency Cepstral Coefficients
KW  - Turkish Speech Recognition
N2  - Bu makalede, Türkçe söylenen sayıların tanınmasına yönelik yeni bir sistem önerilmiştir. Özellik çıkarımı yöntemi olarak Mel frekanslı Kepstral Katsayıları (MFKK) algoritması, her fonetik modelleme olarak ise Gaussian Gizli Markov modeli kullanılmıştır. 7 kadın ve 13 erkekten oluşan 20 denekten toplanan eğitim veri setinde Türkçe rakamların 0'dan 10'a kadar olduğunu söyleyen ses dosyaları vardır. Her dosyada yalıtılmış bir ortamda kaydedilen saniyede 8000 Hz'de örneklenen ve 1 saniye uzunluğunda ses bulunmaktadır. Sistem, farklı kişilerden alınan rastgele kayıtlar kullanarak test edilmiştir. Eğitim dosyaları 220, test dosyaları ise 18 ses içermektedir. Sistem testlerde % 83.3 doğruluk,% 86 hassasiyet ve% 83 hatırlama oranlarına ulaşmıştır.
CR  - Rabiner, Lawrence R., and Biing-Hwang Juang. Fundamentals of speech recognition. Vol. 14. Englewood Cliffs: PTR Prentice Hall, 1993.
CR  - Deller, John R., John HL Hansen, and John G. Proakis. "Discrete-time processing of speech signals." (2000): 595-602
CR  - Motlıcek, Petr. Feature extraction in speech coding and recognition. Technical Report of PhD research internship in ASP Group, OGI-OHSU, http://www. fit. vutbr. cz/∼ motlicek/publi/2002/rep ogi. pdf, 2002
CR  - Hagen, Andreas, Daniel A. Connors, and Bryan L. Pellom. "The analysis and design of architecture systems for speech recognition on modern handheld-computing devices." Proceedings of the 1st IEEE/ACM/IFIP international conference on Hardware/ Software codesign and system synthesis. ACM, 2003
CR  - Ishizuka, Kentaro, and Tomohiro Nakatani. "A feature extraction method using subband based periodicity and aperiodicity decomposition with noise robust frontend processing for automatic speech recognition." Speech communication 48.11 (2006): 1447-1457
CR  - Xu, Min, et al. "HMM-based audio keyword generation." Pacific-Rim Conference on Multimedia. Springer, Berlin, Heidelberg, 2004
CR  - G. Evermann, H. Y. Chan, M. J. F. Gales, T. Hain, X. Liu, D. Mrva, L. Wang,and P. Woodland, “Development of the 2003 CU-HTK conversational telephone speech transcription system,” in Proceedings of ICASSP, Montreal, Canada, 2004
CR  - S. Matsoukas, J.-L. Gauvain, A. Adda, T. Colthurst, C. I. Kao, O. Kimball, L. Lamel, F. Lefevre, J. Z. Ma, J. Makhoul, L. Nguyen, R. Prasad, R. Schwartz, H. Schwenk, and B. Xiang, “Advances in transcription of broadcast news and conversational telephone speech within the combined EARS BBN/LIMSI system,” IEEE Transactions on Audio, Speech and Language Processing, vol. 14, no. 5, pp. 1541–1556, September 2006
CR  - H. Soltau, B. Kingsbury, L. Mangu, D. Povey, G. Saon, and G. Zweig, “The IBM 2004 conversational telephony system for rich transcription,” in Proceedings of ICASSP, Philadelphia, PA, 2005
CR  - J. K. Baker, “The Dragon system — An overview,” IEEE Transactions on Acoustics Speech and Signal Processing, vol. 23, no. 1, pp. 24–29, 1975
CR  - F. Jelinek, “Continuous speech recognition by statistical methods,” Proceedings of IEEE, vol. 64, no. 4, pp. 532–556, 1976
CR  - B. T. Lowerre, The Harpy Speech Recognition System. PhD thesis, Carnegie Mellon, 1976
CR  - B.-H. Juang, “On the hidden Markov model and dynamic time warping for speech recognition — A unified view,” AT and T Technical Journal, vol. 63,no. 7, pp. 1213–1243, 1984
CR  - B.-H. Juang, “Maximum-likelihood estimation for mixture multivariate stochastic observations of Markov chains,” AT and T Technical Journal,vol. 64, no. 6, pp. 1235–1249, 1985
CR  - S. E. Levinson, L. R. Rabiner, and M. M. Sondhi, “An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition,” Bell Systems Technical Journal, vol. 62, no. 4,pp. 1035–1074, 1983
UR  - https://dergipark.org.tr/en/pub/veri/article/647403
L1  - https://dergipark.org.tr/en/download/article-file/908349
ER  -