An algorithm for formant frequency estimation from noise-corrupted speech signals
Keywords:
Acoustic signal processing, Algorithms, Error analysis, Least squares approximations, Frequency estimation algorithms, Natural sentences, Synthetic vowelsAbstract
An algorithm for format frequency estimation from noise-corrupted speech signals is presented. A scheme for frequency domain noise reduction and the ACF of the resulting noise-compensated speech signal is then employed in a modified form of least-squares Yule-Walker equations (LSYWE) to estimate poles of the VT system. A pole selection criterion is estimated for extracting the desired formats that enables the proposed scheme to avoid errors associated with weak formats. The proposed formant frequency estimation algorithm has been tested using different synthetic vowels synthesized using the Klatt synthesizer and natural vowels from the North Texas speech database as well as some natural sentences from the TIMIT speech database. Experimental results on natural and synthetic speech signals show the efficacy of the proposed method in estimating formant frequencies at a moderate to low levels of SNR.Published
How to Cite
Issue
Section
Copyright on articles is held by the author(s). The corresponding author has the right to grant on behalf of all authors and does grant on behalf of all authors, a worldwide exclusive licence (or non-exclusive license for government employees) to the Publishers and its licensees in perpetuity, in all forms, formats and media (whether known now or created in the future)
i) to publish, reproduce, distribute, display and store the Contribution;
ii) to translate the Contribution into other languages, create adaptations, reprints, include within collections and create summaries, extracts and/or, abstracts of the Contribution;
iii) to exploit all subsidiary rights in the Contribution,
iv) to provide the inclusion of electronic links from the Contribution to third party material where-ever it may be located;
v) to licence any third party to do any or all of the above.