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

Jonathan Darch, Ben P. Milner

Research output: Contribution to conferencePaper

2 Citations (Scopus)


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.
Original languageEnglish
Publication statusPublished - 2006
EventICSLP 9th International Conference on Spoken Language Processing - Pittsburgh, United States
Duration: 17 Sep 200621 Sep 2006


ConferenceICSLP 9th International Conference on Spoken Language Processing
Abbreviated titleInterspeech 2006
Country/TerritoryUnited States

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