Persian phonemes recognition using PPNet

In this paper a new approach for recognition of Persian phonemes on the PCVC speech dataset is proposed. Nowadays deep neural networks are playing main rule in classification tasks. However the best results in speech recognition are not as good as human recognition rate yet. Deep learning techniques are shown their outstanding performance over so many classification tasks like image classification, document classification, etc. Also in some tasks their performance were even better than human. So the reason why ASR (automatic speech recognition) systems are not as good as the human speech recognition system is mostly depend on features of data is fed to deep neural networks. In this research first sound samples are cut for exact extraction of phoneme sounds in 50ms samples. Then phonemes are grouped in 30 groups; Containing 23 consonants, 6 vowels and a silence phoneme. STFT (Short time Fourier transform) is applied on them and Then STFT results are given to PPNet (A new deep convolutional neural network architecture) classifier and a total average of 75.87% accuracy is reached which is the best result ever compared to other algorithms on Separated Persian phonemes (Like in PCVC speech dataset).