Distinctive Phonetic Features Modeling and Extraction Using Deep Neural Networks

@article{Seddiq2019DistinctivePF,
  title={Distinctive Phonetic Features Modeling and Extraction Using Deep Neural Networks},
  author={Y. Seddiq and Y. Alotaibi and Sid-Ahmed Selouani and A. Meftah},
  journal={IEEE Access},
  year={2019},
  volume={7},
  pages={81382-81396}
}
Feature extraction is a critical stage of digital speech processing systems. Quality of features is of great importance to provide a solid foundation upon which the subsequent stages stand. Distinctive phonetic features (DPFs) are one of the most representative features of the speech signals. The significance of DPFs is in their ability to provide abstract description of the places and manners of articulation of the language phonemes. A phoneme’s DPF element reflects unique articulatory… Expand
4 Citations
Optimizing Arabic Speech Distinctive Phonetic Features and Phoneme Recognition Using Genetic Algorithm
  • 1
  • PDF
Deep Learning-Based Detection of Articulatory Features in Arabic and English Speech
  • Highly Influenced
  • PDF
Deep Learning: Current State
  • 3

References

SHOWING 1-10 OF 39 REFERENCES
Distinctive phonetic feature extraction for robust speech recognition
  • 30
Distinctive phonetic feature (DPF) based phone segmentation using hybrid neural networks
  • 10
  • PDF
Exploring how deep neural networks form phonemic categories
  • 63
  • PDF
Deep Belief Networks using discriminative features for phone recognition
  • 281
  • PDF
Canonicalization of feature parameters for automatic speech recognition
  • 8
  • PDF
A new look at the automatic mapping between Arabic distinctive phonetic features and acoustic cues
  • 2
Designing multiple distinctive phonetic feature extractors for canonicalization by using clustering technique
  • 5
  • PDF
...
1
2
3
4
...