English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the proposed work, the techniques of wavelet transform (WT) and neural network were introduced for speech based text-independent speaker identification and Arabic vowel recognition. The linear prediction coding coefficients (LPCC) of discrete wavelet transform (DWT) upon level 3 features extraction method was developed. Feature vector fed to probabilistic neural networks (PNN) for classification. The functions of features extraction and classification are performed using the wavelet transform and neural networks (DWTPNN) expert system. The declared results show that the proposed method can make an powerful analysis with average identification rates reached 93. Two published methods were investigated for comparison. The best recognition rate selection obtained was for framed DWT. Discrete wavelet transform was studied to improve the system robustness against the noise of 0dB. Our investigation of speaker-independent Arabic vowels classifier system performance is performed via several experiments depending on vowel type. The declared results show that the proposed method can make an effectual analysis with identification rates may reach 93%. In general, a speaker identification system can be implemented by observing the voiced/unvoiced components or through analyzing the energy distribution of utterances. A number of digital signal processing algorithms, such as LPC technique (Adami & Barone, 2001; Tajima, Port, & Dalby, 1997), Mel frequency cepstral coefficients (MFCCs) (Mashao & Skosan, 2006; Sroka & Braida, 2005; Kanedera, Arai, Hermansky & Pavel, 1999; Daqrouq & Al-Faouri, 2010), DWT (Fonseca, Guido, Scalassara, Maciel, & Pereira, 2007) and wavelet packet transform (WPT) (Lung, 2006; Zhang & Jiao, 2004) are extensively utilized. In the beginning of 1990s, Mel frequency cepstral technique became the most widely used technique for recognition purposes due to its aptitude to represent the speech spectrum in a compacted form (Sarikaya & ansen, 2000). Actually, MFCCs simulate the model of umans’ auditory perception and have been proven to be very effective in automatic speech recognition system and modeling the individual frequency components of speech signals.

Discrete Wavelet Transform & Linear Prediction Coding Based Method for Speech Recognition via Neural Network