Formant Tracking Using Dilated Convolutional Networks Through Dense Connection with Gating Mechanism
This work addresses formant tracking for speech processing researchers, presenting an incremental improvement over existing methods.
The paper tackled formant tracking in speech processing by proposing a modified Temporal Convolutional Network with dense connections and a gating mechanism, achieving an 8.2% mean absolute percent error compared to baselines of 9.4%, 9.1%, and 8.9%.
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).