The '''PCVCPersian Speechconsonant Datasetvowel combination speech dataset''' is a [[Modern Persian]] [[speech corpus]] for [[speech recognition]]. The dataset contains sound samples of [[Modern Persian]] combination of [[vowel]] and [[consonant]] phonemes from different speakers. Every sound sample contains just one consonant and one vowel So it is somehow labeled in phoneme level. This dataset contains of 23 Persian consonants and 6 vowels. The sound samples are all possible combinations of vowels and consonants (138 samples for each speaker). The sample rate of all speech samples is 48000 which means there are 48000 sound samples in every 1 second. Every sound sample starts with consonant then continues with vowel. In each sample, in average, 0.5 second of each sample is speech and the rest is silence. Each sound sample ends with silence.<ref> Saber MalekzadeH, Mohammad Hossein Gholizadeh, Seyed Naser Razavi {{cite paper |title=Full Persian Vowel recognition with MFCC and ANN on PCVC speech dataset |url=http://bayanbox.ir/download/2723849504007807268/Full-Persian-Vowel-recognition-with-MFCC-and-ANN-on-PCVC-speech-dataset.pdf }} 5th International conference of electrical engineering, computer science and information technology, Iran, Tehran, 2018.</ref> All of sound samples are denoised with "Adaptive noise reduction" algorithm.<ref>{{cite paper |title=PCVC GitHub page |url=https://github.com/S-Malek/PCVC }}</ref>
Compared to Farsdat speech dataset<ref>Bijankhan, M., Sheikhzadegan, J., Roohani, M. R., Samareh, Y., Lucas, C., & Tebyani, M. (1994). FARSDAT-The Speech Database of Farsi Spoken Language. The Proceedings of the Australian Conference on Speech Science and Technology (Vol. 2, pp. 826–831).</ref> and Persian Speechspeech Corpuscorpus<ref>Halabi, Nawar (2016). Modern Standard Persian Phonetics for Speech Synthesis. University of Southampton, School of Electronics and Computer Science.</ref> it is more easy to use because it is prepared in .mat data files.<ref>{{cite paper |title= Access and change variables directly in MAT-files, without loading into memory. |url=https://uk.mathworks.com/help/matlab/ref/matfile.html }}</ref> Also it is more based on phoneme based separation and all samples are denoised.
