Formant prediction from MFCC vectors

Darch, Jonathan, Milner, B.P. and Shao, X. (2004) Formant prediction from MFCC vectors. In: COST278 and ISCA Tutorial and Research Workshop (ITRW) on Robustness Issues in Conversational Interaction (Robust2004), 2004-08-30 - 2004-08-31.

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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 MFCC vectors and formant vectors 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 from all clusters. Experimental results are presented using the ETSI Aurora connected digit database and show that the predicted formant frequency is within 3.25% of the reference formant frequency, as measured from hand-corrected formant tracks.

Item Type: Conference or Workshop Item (Poster)
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 20 Jul 2011 19:31
Last Modified: 12 May 2019 23:33
URI: https://ueaeprints.uea.ac.uk/id/eprint/22473
DOI:

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