Estimation of Severity of Speech Disability through Speech Envelope

In this paper, envelope detection of speech is discussed to distinguish the pathological cases of speech disabled children. The speech signal samples of children of age between five to eight years are considered for the present study. These speech signals are digitized and are used to determine the speech envelope. The envelope is subjected to ratio mean analysis to estimate the disability. This analysis is conducted on ten speech signal samples which are related to both place of articulation and manner of articulation. Overall speech disability of a pathological subject is estimated based on the results of above analysis.Comment: 8 pages,4 Figures,Signal & Image Processing Journal AIRC

Automatic Speech Recognition for Indonesian using Linear Predictive Coding (LPC) and Hidden Markov Model (HMM)

Speech recognition is influential signal processing in communication technology. Speech recognition has allowed software to recognize the spoken word. Automatic speech recognition could be a solution to recognize the spoken word. This application was developed using Linear Predictive Coding (LPC) for feature extraction of speech signal and Hidden Markov Model (HMM) for generating the model of each the spoken word. The data of speech used for training and testing was produced by 10 speaker (5 men and 5 women) whose each speakers spoke 10 words and each of words spoken for 10 times. This research is tested using 10-fold cross validation for each pair LPC order and HMM states. System performance is measured based on the average accuracy testing from men and women speakers. According to the test results that the amount of HMM states affect the accuracy of system and the best accuracy is 94, 20% using LPC order =13 and HMM state=16

Use of Mel Frequency Cepstral Coefficients for Automatic Pathology Detection on Sustained Vowel Phonations: Mathematical and Statistical Justification

This paper presents a justification for the use of MFCC parameters in automatic pathology detection on speech. While such an application has produced good results up to now, only partial explanations to this good performance had been given before. The herein exposed explanation consists of an interpretation of the mathematical transformations involved in MFCC calculation and a statistical analysis that confirms the conclusions drawn from the theoretical reasoning