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

Darch, Jonathan and Milner, Ben P. (2006) HMM-based MAP Prediction of Voiced and Unvoiced Formant Frequencies from Noisy MFCC Vectors. In: ICSLP 9th International Conference on Spoken Language Processing, 2006-09-17 - 2006-09-21.

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

Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
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
Depositing User:	Vishal Gautam
Date Deposited:	21 May 2011 11:41
Last Modified:	10 Dec 2024 01:14
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22475
DOI:

Actions (login required)

View Item