Predicting formant frequencies from MFCC vectors

Jonathan Darch, Ben Milner, Xu Shao, Saeed Vaseghi, Qin Yan

Research output: Contribution to conferencePaper

8 Citations (Scopus)


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.
Original languageEnglish
Number of pages4
Publication statusPublished - 2005
EventIEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) - Philadelphia, United States
Duration: 18 Mar 200523 Mar 2005


ConferenceIEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)
Country/TerritoryUnited States

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