Volume 26, Issue 2, September 2016, Pages 384–389
Akankhya Sarmah1 and Kashyapi Kalita2
1 Electronics and Telecommunication Engineering, Assam Engineering College, Guwahati, Assam, India
2 Electronics and Telecommunication Engineering, Assam Engineering College, Guwahati, Assam, India
Original language: English
Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The length of the vocal tract is correlated with body size which can be determined by using formant frequencies in speech. The interconnections between vocal tract length and formant frequencies are explored here. Recorded and computer-synthesized vowel sounds are used to gauge the vocal tract length of a speaker under consideration. Vocal tract length assessment may play an important role in ‘vocal tract normalization’, which is crucial for speech perception and language acquisition. Vocal tract length is analyzed by applying two methods namely linear predictive coding (LPC) and vocal tract constriction (VTC). Speech signal is produced by the convolution of excitation source and time varying vocal tract system components. These excitation and vocal tract components are to be separated from the available speech signal to study these components independently. To reduce the complexity of deconvolving the given speech into excitation and vocal tract system components the ‘Linear Prediction analysis’ is developed. The VTC evidence gives a measure of the very low frequency component present in the signal and hence gives different range of frequency values for different types of sounds. Zero frequency filtering (ZFF) is used to give an approximate measure of vocal tract constriction in terms of the low frequency component present in the speech signal.
Author Keywords: Vocal tract length, Formant frequencies, Speech signal, VTC, LPC.
Akankhya Sarmah1 and Kashyapi Kalita2
1 Electronics and Telecommunication Engineering, Assam Engineering College, Guwahati, Assam, India
2 Electronics and Telecommunication Engineering, Assam Engineering College, Guwahati, Assam, India
Original language: English
Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The length of the vocal tract is correlated with body size which can be determined by using formant frequencies in speech. The interconnections between vocal tract length and formant frequencies are explored here. Recorded and computer-synthesized vowel sounds are used to gauge the vocal tract length of a speaker under consideration. Vocal tract length assessment may play an important role in ‘vocal tract normalization’, which is crucial for speech perception and language acquisition. Vocal tract length is analyzed by applying two methods namely linear predictive coding (LPC) and vocal tract constriction (VTC). Speech signal is produced by the convolution of excitation source and time varying vocal tract system components. These excitation and vocal tract components are to be separated from the available speech signal to study these components independently. To reduce the complexity of deconvolving the given speech into excitation and vocal tract system components the ‘Linear Prediction analysis’ is developed. The VTC evidence gives a measure of the very low frequency component present in the signal and hence gives different range of frequency values for different types of sounds. Zero frequency filtering (ZFF) is used to give an approximate measure of vocal tract constriction in terms of the low frequency component present in the speech signal.
Author Keywords: Vocal tract length, Formant frequencies, Speech signal, VTC, LPC.
How to Cite this Article
Akankhya Sarmah and Kashyapi Kalita, “Determining and comparing vocal tract length using vocal tract constrictions and linear predictive coding,” International Journal of Innovation and Scientific Research, vol. 26, no. 2, pp. 384–389, September 2016.
