Many works on speech processing have dealt with
auto-regressive (AR) models for spectral envelope and
formant frequency estimation, mostly focusing on the
estimation of the AR parameters. However, it is also
interesting to be able to directly estimate the formant
frequencies, or equivalently the poles of the AR filter.
To tackle this issue, we propose in this paper to
decompose the signal onto several bases, one for each
formant, taking advantage of recent works on nonnegative
matrix factorization (NMF) for the estimation stage,
further refined by sparsity and smoothness penalties. The
results are encouraging, and the proposed system provides
formant tracks which seem robust enough to be used in
different applications such as phonetic analysis, emotion
detection or as visual cue for computer-aided
pronunciation training applications. The model can also
be extended to deal with multiple-speaker signals.