Refining a Deep Learning-based Formant Tracker using Linear Prediction
  Methods
Paavo Alku Sudarsana Reddy Kadiri Dhananjaya Gowda
In this study, formant tracking is investigated by refining the formants
tracked by an existing data-driven tracker, DeepFormants, using the formants
estimated in a model-driven manner by linear prediction (LP)-based methods. As
LP-based formant estimation methods, conventional covariance analysis (LP-COV)
and the recently proposed quasi-closed phase forward-backward (QCP-FB) analysis
are used. In the proposed refinement approach, the contours of the three lowest
formants are first predicted by the data-driven DeepFormants tracker, and the
predicted formants are replaced frame-wise with local spectral peaks shown by
the model-driven LP-based methods. The refinement procedure can be plugged into
the DeepFormants tracker with no need for any new data learning. Two refined
DeepFormants trackers were compared with the original DeepFormants and with
five known traditional trackers using the popular vocal tract resonance (VTR)
corpus. The results indicated that the data-driven DeepFormants trackers
outperformed the conventional trackers and that the best performance was
obtained by refining the formants predicted by DeepFormants using QCP-FB
analysis. In addition, by tracking formants using VTR speech that was corrupted
by additive noise, the study showed that the refined DeepFormants trackers were
more resilient to noise than the reference trackers. In general, these results
suggest that LP-based model-driven approaches, which have traditionally been
used in formant estimation, can be combined with a modern data-driven tracker
easily with no further training to improve the tracker's performance.