Predicting formant frequencies from MFCC vectors

Darch, Jonathan, Milner, Ben, Shao, Xu, Vaseghi, Saeed and Yan, Qin (2005) Predicting formant frequencies from MFCC vectors. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2005-03-18 - 2005-03-23.

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Abstract

This work proposes a novel method of predicting formant frequencies from a stream of mel-frequency cepstral coefficients (MFCC) feature vectors. Prediction is based on modelling the joint density of MFCCs and formant frequencies using a Gaussian mixture model (GMM). Using this GMM and an input MFCC vector, two maximum a posteriori (MAP) prediction methods are developed. The first method predicts formants from the closest, in some sense, cluster to the input MFCC vector, while the second method takes a weighted contribution of formants predicted from all clusters. Experimental results are presented using the ETSI Aurora connected digit database and show that predicted formant frequencies are within 3.2% of reference formant frequencies.

Item Type:	Conference or Workshop Item (Paper)
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Visual Computing and Signal Processing Faculty of Science > Research Groups > Smart Emerging Technologies (former - to 2025) Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Cyber Intelligence and Networks
Depositing User:	Vishal Gautam
Date Deposited:	14 Jun 2011 11:25
Last Modified:	28 Mar 2025 13:22
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22474
DOI:	10.1109/ICASSP.2005.1415270

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