An Investigation into the Correlation and Prediction of Acoustic Speech Features from MFCC Vectors

Abstract

This work develops a statistical framework to predict acoustic features (fundamental frequency, formant frequencies and voicing) from MFCC vectors. An analysis of correlation between acoustic features and MFCCs is made both globally across all speech and within phoneme classes, and also from speaker-independent and speaker-dependent speech. This leads to the development of both a global prediction method, using a Gaussian mixture model (GMM) to model the joint density of acoustic features and MFCCs, and a phoneme-specific prediction method using a combined hidden Markov model (HMM)-GMM. Prediction accuracy measurements show the phoneme-dependent HMM-GMM system to be more accurate which agrees with the correlation analysis. Results also show prediction to be more accurate from speaker-dependent speech which also agrees with the correlation analysis.