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, University of East Anglia.

Full text not available from this repository. (Request a copy)

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
UEA Research Groups:	Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI
Related URLs:	http://lemur.cmp.uea.ac.uk/Research/robu...
Depositing User:	Vishal Gautam
Date Deposited:	20 Jul 2011 19:31
Last Modified:	10 Dec 2024 01:15
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22473
DOI:

Actions (login required)

View Item