Single and Multiple ${ F}_{0}$ Contour Estimation Through Parametric Spectrogram Modeling of Speech in Noisy Environments

This paper proposes a novel F₀ contour estimation algorithm based on a precise parametric description of the voiced parts of speech derived from the power spectrum. The algorithm is able to perform in a wide variety of noisy environments as well as to estimate the F₀s of cochannel concurrent speech. The speech spectrum is modeled as a sequence of spectral clusters governed by a common F₀ contour expressed as a spline curve. These clusters are obtained by an unsupervised 2-D time-frequency clustering of the power density using a new formulation of the EM algorithm, and their common F ₀ contour is estimated at the same time. A smooth F₀ contour is extracted for the whole utterance, linking together its voiced parts. A noise model is used to cope with nonharmonic background noise, which would otherwise interfere with the clustering of the harmonic portions of speech. We evaluate our algorithm in comparison with existing methods on several tasks, and show 1) that it is competitive on clean single-speaker speech, 2) that it outperforms existing methods in the presence of noise, and 3) that it outperforms existing methods for the estimation of multiple F₀ contours of cochannel concurrent speech