Autoregressive modelling for linear prediction of ultrasonic speech

Ultrasonic speech is a novel technology which implies exciting human vocal tract (VT) with an ultrasonic signal to provide a speech mode in the ultrasonic frequency range. This has several applications including speech-aid prostheses for voice-loss patients, silent speech interfaces, secure mode of communication in mobile phones and speech therapy. Linear prediction has recently been proven to be applicable for feature extraction of ultrasonic propagation inside the VT. The authors have proposed that averaging the predictor coefficients obtained from multiple receiving points is a viable approach for autoregressive (AR) modelling of ultrasonic speech. In support of the previous theoretical work, this paper presents experimental results of implementing the averaging method, using finite element analysis of ultrasonic propagation inside the VT configuration for nine English vowels. A comparison of the results with the conventional method of least squares error (LSE) used in room acoustics shows that averaging outperforms LSE in terms of determining the location of poles in the AR modelling of ultrasonic speech and demonstrates higher robustness to variations of the LPC order.