Comparison of several acoustic features for the vowel sequence reproduction of a talking robot

This study compares several acoustic features for developing an automatic vowel sequence reproduction system for a talking robot, which is a mechanical vocalization system modeling the human articulatory system. Matlab-based control system is used to analyze a recorded sound and drives the articulatory motors of the talking robot. A novel method based on short-time energy analysis is used to extract a human speech and translate into a sequence of sound elements for the sequence of vowels reproduction. Then, several phonemes detection methods including the direct cross-correlation analysis, the linear predictive coding (LPC) association, the partial correlation (PARCOR) coefficients analysis, and the formant frequencies comparison are applied to each sound element to give the corrected command for the talking robot to repeat the sound sequentially. Finally, experiments to compare these techniques and verify the working behavior of the robot are performed. The result of the tests indicates that the robot is able to repeat a sequence of vowels spoken by a human with a successful rate of more than 70% for the PARCOR analysis technique and the formant frequencies comparison technique. The greatest accuracy for repeating the sequence is given by the formant comparison method, while the direct cross-correlation method delivers the least accuracy.