Ninth International Conference on Spoken Language Processing

Pittsburgh, PA, USA
September 17-21, 2006

HMM-Based MAP Prediction of Voiced and Unvoiced Formant Frequencies from Noisy MFCC Vectors

Jonathan Darch, Ben Milner

University of East Anglia, UK

This paper describes how formant frequencies of voiced and unvoiced speech can be predicted from mel-frequency cepstral coefficients (MFCC) vectors using maximum a posteriori (MAP) estimation within a hidden Markov model (HMM) framework. Gaussian mixture models (GMMs) are used to model the local joint density of MFCCs and formant frequencies. More localised prediction is achieved by modelling speech using voiced, unvoiced and non-speech GMMs for every state of each model of a set of HMMs. To predict formant frequencies from a MFCC vector, first a prediction of the speech class (voiced, unvoiced or non-speech) is made. Formant frequencies are predicted from voiced and unvoiced speech using a MAP estimation made using the state-specific GMMs. This 'eHMM-GMM' prediction of speech class and formant frequencies was evaluated on a male 5000 word unconstrained large vocabulary speaker-independent database.

Full Paper

Bibliographic reference.  Darch, Jonathan / Milner, Ben (2006): "HMM-based MAP prediction of voiced and unvoiced formant frequencies from noisy MFCC vectors", In INTERSPEECH-2006, paper 1540-Tue2A1O.3.