Open AccessApplying the Artificial Neural Networks with Multiwavelet Transform on Phoneme recognition
Author(s) -
Baydaa Jaffer Al-Khafaji,
May A. Salih,
Shaymaa AbdulHussein Shnain,
Omar Adel Rashid,
Abdulla Adil Rashid,
Moheeb Tariq Hussein
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1804/1/012040
Subject(s) - computer science , speech recognition , artificial neural network , redundancy (engineering) , speech processing , upload , time delay neural network , artificial intelligence , operating system
There are several advantages of Phoneme recognition. identification. It is easier to use speech for data entrance spoken communication for data ingress than other tools. It allows writing user-friendly data entrance exploiter-friendly data ingress programs. There are several difficulties in speech voice communication recognition. One of these difficulties is noise. Variability in speech is another problem. Even the speech of same speaker varies. The ability of artificial neural networks to generalize and optimize more quickly than some conventional algorithms algorithmic rule has been observed in different areas of research inquiry such as speech and pattern convention recognition, financial forecasting prognostication, image data compression and noise reduction simplification in signal processing. Neural networks take advantage of the redundancy incorporated in their distributed processing structures the proposed system depends on Artificial Neural Networks Network as decision making qualification algorithm to find the best match peer for the tested phonemes. Phoneme. The data used in this project are Arabic phonemes language phoneme stored as 8-bit mono infectious mononucleosis 8000Hz PCM WAVE Sound Auditory sensation file. The results showed that the accuracy of the proposed system is 98% recognizes the phonemes efficiently.