Modelling and Estimation of the Fundamental Frequency of Speech Using a Hidden Markov Model

John Taylor, Ben Milner

Research output: Contribution to conferencePaperpeer-review


This paper proposes using a hidden Markov model (HMM) to model a speech signal in terms of its speech class (voiced, unvoiced and nonspeech) and for voiced speech its fundamental frequency. States of the HMM represent unvoiced speech and nonspeech with multiple voiced states that model different fundamental frequencies. The transition matrix of the HMM models temporal changes in speech class and the time-varying fundamental frequency contour. The model is then applied to voicing and fundamental frequency estimation by extracting acoustic features from a speech signal and then applying Viterbi decoding. Experimental results are presented that investigate the estimation accuracy of the proposed system and a comparison is made against conventional methods.
Original languageEnglish
Number of pages5
Publication statusPublished - 2013
EventInterspeech -
Duration: 5 Aug 2013 → …


Period5/08/13 → …

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