  Besides the well-known classification task, these days neural networks are
frequently being applied to generate or transform data, such as images and
audio signals. In such tasks, the conventional loss functions like the mean
squared error (MSE) may not give satisfactory results. To improve the
perceptual quality of the generated signals, one possibility is to increase
their similarity to real signals, where the similarity is evaluated via a
discriminator network. The combination of the generator and discriminator nets
is called a Generative Adversarial Network (GAN). Here, we evaluate this
adversarial training framework in the articulatory-to-acoustic mapping task,
where the goal is to reconstruct the speech signal from a recording of the
movement of articulatory organs. As the generator, we apply a 3D convolutional
network that gave us good results in an earlier study. To turn it into a GAN,
we extend the conventional MSE training loss with an adversarial loss component
provided by a discriminator network. As for the evaluation, we report various
objective speech quality metrics such as the Perceptual Evaluation of Speech
Quality (PESQ), and the Mel-Cepstral Distortion (MCD). Our results indicate
that the application of the adversarial training loss brings about a slight,
but consistent improvement in all these metrics.
