@article{article_647403, title={Turkish Speech recognition using Mel-frequency cepstral coefficients(MFCC) and Hidden Markov Model (HMM)}, journal={Veri Bilimi}, volume={2}, pages={39–44}, year={2019}, author={Kocer, Hasan Erdinc and Ahmed, Mustafa Cumaah}, keywords={Gizli Markov Modeli,Mel-Frekans Kepstral Katsayılar,Türkçe Konuşma Tanıma}, abstract={<p> <span style="font-size:10pt;font-family:Cambria, serif;">In this paper, a new Turkish
spoken number recognition system proposed. The Mel-frequency cepstral
coefficients (MFCC) algorithm used as a feature extraction method, the Gaussian
Hidden Markov model, used for numbers phonemes modeling where each number has a
Markov model. The system trained on a dataset collected from 20 subjects that
includes 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 has
one-second length and recorded in an isolated environment. We tested the system
using random records for different people. The training files include 220 audio
record and testing files include 18 audio record. The system achieves %83.3
accuracy, %86 precision, and %83 recall rates. </span> <br /> </p>}, number={2}, publisher={Murat GÖK}