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The Recognition of Persian Phonemes Using PPNet.

Saber Malekzadeh1,2, Mohammad Hossein Gholizadeh1, Seyed Naser Razavi3

  • 1Department of Electrical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran.

Journal of Medical Signals and Sensors
|July 18, 2020
PubMed
Summary

This study introduces a new deep learning method for recognizing Persian phonemes, achieving 75.87% accuracy in Persian consonant-vowel combination (PCVC) speech datasets. This advancement improves automatic speech recognition systems.

Keywords:
PPNetPersianPersian consonant-vowel combinationshort-time Fourier transformspeech recognition

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Area of Science:

  • Speech processing and recognition
  • Artificial intelligence and machine learning

Background:

  • Automatic speech recognition (ASR) systems struggle to match human accuracy.
  • Deep neural networks (NNs) show promise but are limited by input data features.
  • Persian phoneme recognition is a specific challenge within ASR.

Purpose of the Study:

  • To propose a novel approach for accurate Persian phoneme recognition.
  • To enhance the performance of deep neural networks in speech classification tasks.
  • To improve the quality of automatic speech recognition for Persian language.

Main Methods:

  • Phoneme sounds were extracted from speech samples in 50 ms segments.
  • Phonemes were categorized into 30 groups: 23 consonants, 6 vowels, and silence.
  • Short-time Fourier transform was applied, and data was fed into a PPNet classifier.

Main Results:

  • The proposed PPNet deep convolutional neural network architecture achieved an average accuracy of 75.87%.
  • This represents the highest accuracy reported for Persian phoneme recognition on the PCVC dataset.
  • The method demonstrates superior performance compared to existing algorithms.

Conclusions:

  • The developed method effectively recognizes mono-phonemes in Persian speech.
  • This technique can be integrated into speech transcription systems for improved word selection.
  • The approach offers a pathway to more human-like speech recognition capabilities.