Wavelet Transform based multistage speaker feature tracking identification system using Linear Prediction Coefficient

2009 ◽  
Author(s):  
Khaled Daqrouq ◽  
Abdel-Rahman Al-Qawasmi ◽  
Wael Al-Sawalmeh ◽  
Tareq Abu Hilal
Keyword(s):  
Wavelet Transform ◽  
Feature Tracking ◽  
Linear Prediction ◽  
Identification System ◽  
Linear Prediction Coefficient ◽  
Prediction Coefficient

LPC AND ITS DERIVATIVES FOR STUTTERED SPEECH RECOGNITION

2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Sabur Ajibola Alim ◽  
Nahrul Khair Alang Rashid ◽  
Wahju Sediono ◽  
Nik Nur Wahidah Nik Hashim
Keyword(s):  
Feature Extraction ◽  
Linear Prediction ◽  
Misclassification Rate ◽  
Speech Pathology ◽  
Normal Flow ◽  
University College London ◽  
Linear Prediction Coefficient ◽  
Prediction Coefficient ◽  
Spectral Frequency ◽  
Line Spectral Frequency

Stuttering or stammering is disruptions in the normal flow of speech by dysfluencies, which can be repetitions or prolongations of phoneme or syllable. Stuttering cannot be permanently cured, though it may go into remission or stutterers can learn to shape their speech into fluent speech with an appropriate speech pathology treatment. Linear Prediction Coefficient (LPC), Linear Prediction Cepstral Coefficient (LPCC) and Line Spectral Frequency (LSF) were used for the feature extraction, while Multilayer Perceptron (MLP) was used as the classifier. The samples used were obtained from UCLASS (University College London Archive of Stuttered Speech) release 1. The LPCC-MLP system had the highest overall sensitivity, precision and the lowest overall misclassification rate. LPCC-MLP system had challenges with F3, the sensitivity of the system to F3 was negligible, similarly, the precision was moderate and the misclassification rate was negligible, but above 10%. 

Features Extraction for Lhasa Tibetan Speech Recognition

2014 ◽  
Vol 571-572 ◽  
pp. 205-208
Author(s):  
Guan Yu Li ◽  
Hong Zhi Yu ◽  
Yong Hong Li ◽  
Ning Ma
Keyword(s):  
Speech Recognition ◽  
Linear Prediction ◽  
Recognition System ◽  
Continuous Speech Recognition ◽  
Mel Frequency Cepstral Coefficients ◽  
Linear Prediction Coefficient ◽  
Speech Feature ◽  
Perceptual Linear Prediction ◽  
Prediction Coefficient ◽  
Speech Feature Extraction

Speech feature extraction is discussed. Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction coefficient (PLP) method is analyzed. These two types of features are extracted in Lhasa large vocabulary continuous speech recognition system. Then the recognition results are compared.

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