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Autoregressive Modelling for Linear Prediction of Ultrasonic Speech

Ahmadi, Farzaneh and McLoughlin, Ian V. and Sharifzadeh, Hamid R. (2010) Autoregressive Modelling for Linear Prediction of Ultrasonic Speech. In: 11th Annual Conference of the International Speech Communication Association 2010. International Speech Communication Association, pp. 829-832. ISBN 978-1-61782-123-3. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:48769)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.

Abstract

Ultrasonic speech is a novel research area with significant applications: as a speech-aid prosthesis for patients with voice box difficulties, silent speech interfaces, secure mode of communication in mobile phones and as a communication medium in high noise industrial environments. Feature extraction is a critical part of the ultrasonic speech system. Linear prediction analysis (LPA) has been recently proven to be viable for extracting features from the three dimensional ultrasonic propagation in the vocal tract (VT). A one-dimensional autoregressive model based on averaging the LP coefficients, analysed in different recording positions has been investigated by the authors to fit the LF ultrasonic resonances of the VT. To reach a state of maturity for the LPA of ultrasonic speech and in continuum of the previous work, this paper compares the application of two major conventional methods of averaging and least squares error - already applied in room acoustics - for deriving the coefficients in autoregressive modelling of ultrasonic speech.

Item Type: Book section
Uncontrolled keywords: ultrasonic speech, linear prediction analysis, autoregressive modelling, averaging, least squares
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Ian McLoughlin
Date Deposited: 07 Sep 2015 15:24 UTC
Last Modified: 16 Nov 2021 10:19 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48769 (The current URI for this page, for reference purposes)

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