Abstract
This paper proposes a method of predicting the formant frequencies of a frame of speech from its mel-frequency cepstral coefficient (MFCC) representation. Prediction is achieved through the creation of a Gaussian mixture model (GMM) which models the joint density of formant frequencies and MFCCs. Using this GMM and an input MFCC vector, a maximum a posteriori (MAP) prediction of the formant frequencies is generated. Formant prediction accuracy is evaluated on both a constrained vocabulary connected digits database and on a 5000 word large vocabulary database. Experiments first examine the accuracy of formant frequency prediction as the number of clusters in the GMM is varied with a best formant frequency prediction error of 3.72% being obtained. Secondly the effect of noise on formant prediction accuracy is examined. A fall in accuracy is observed with reducing signal-to-noise ratios, but by using a GMM matched to the noise conditions formant prediction accuracy is significantly improved.
Original language | English |
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Pages | 1129-1132 |
Number of pages | 4 |
Publication status | Published - Sep 2005 |
Event | 9th European Conference on Speech Communication and Technology - Lisbon, Portugal Duration: 4 Sep 2005 → 8 Sep 2005 |
Conference
Conference | 9th European Conference on Speech Communication and Technology |
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Abbreviated title | INTERSPEECH-2005 |
Country/Territory | Portugal |
City | Lisbon |
Period | 4/09/05 → 8/09/05 |