  Formant tracking is one of the most fundamental problems in speech
processing. Traditionally, formants are estimated using signal processing
methods. Recent studies showed that generic convolutional architectures can
outperform recurrent networks on temporal tasks such as speech synthesis and
machine translation. In this paper, we explored the use of Temporal
Convolutional Network (TCN) for formant tracking. In addition to the
conventional implementation, we modified the architecture from three aspects.
First, we turned off the "causal" mode of dilated convolution, making the
dilated convolution see the future speech frames. Second, each hidden layer
reused the output information from all the previous layers through dense
connection. Third, we also adopted a gating mechanism to alleviate the problem
of gradient disappearance by selectively forgetting unimportant information.
The model was validated on the open access formant database VTR. The experiment
showed that our proposed model was easy to converge and achieved an overall
mean absolute percent error (MAPE) of 8.2% on speech-labeled frames, compared
to three competitive baselines of 9.4% (LSTM), 9.1% (Bi-LSTM) and 8.9% (TCN).
