  In this paper a new method for recognition of consonant-vowel phonemes
combination on a new Persian speech dataset titled as PCVC (Persian
Consonant-Vowel Combination) is proposed which is used to recognize Persian
phonemes. In PCVC dataset, there are 20 sets of audio samples from 10 speakers
which are combinations of 23 consonant and 6 vowel phonemes of Persian
language. In each sample, there is a combination of one vowel and one
consonant. First, the consonant phoneme is pronounced and just after it, the
vowel phoneme is pronounced. Each sound sample is a frame of 2 seconds of
audio. In every 2 seconds, there is an average of 0.5 second speech and the
rest is silence. In this paper, the proposed method is the implementations of
the MFCC (Mel Frequency Cepstrum Coefficients) on every partitioned sound
sample. Then, every train sample of MFCC vector is given to a multilayer
perceptron feed-forward ANN (Artificial Neural Network) for training process.
At the end, the test samples are examined on ANN model for phoneme recognition.
After training and testing process, the results are presented in recognition of
vowels. Then, the average percent of recognition for vowel phonemes are
computed.
