Speaker-Independent Mel-Cepstrum Estimation from Articulator Movements Using D-Vector Input

We describe a speaker-independent mel-cepstrum estimation system which accepts electromagnetic articulography (EMA) data as input. The system collects speaker information with d-vectors generated from the EMA data. We have also investigated the effect of speaker independence in the input vectors given to the mel-cepstrum estimator. This is accomplished by introducing a two-stage network, where the first stage is trained to output EMA sequences that are averaged across all speakers on a per-triphone basis (and so are speaker-independent) and the second receives these as input for mel-cepstrum estimation. Experimental results show that using the d-vectors can improve the performance of mel-cepstrum estimation by 0.19 dB with regard to mel-cepstrum distortion in the closed-speaker test set. Additionally, giving triphone-averaged EMA data to a mel-cepstrum estimator is shown to improve the performance by a further 0.16 dB, which indicates that the speaker-independent input has a positive effect on mel-cepstrum estimation.