  We propose the first method to adaptively modify the duration of a given
speech signal. Our approach uses a Bayesian framework to define a latent
attention map that links frames of the input and target utterances. We train a
masked convolutional encoder-decoder network to produce this attention map via
a stochastic version of the mean absolute error loss function; our model also
predicts the length of the target speech signal using the encoder embeddings.
The predicted length determines the number of steps for the decoder operation.
During inference, we generate the attention map as a proxy for the similarity
matrix between the given input speech and an unknown target speech signal.
Using this similarity matrix, we compute a warping path of alignment between
the two signals. Our experiments demonstrate that this adaptive framework
produces similar results to dynamic time warping, which relies on a known
target signal, on both voice conversion and emotion conversion tasks. We also
show that our technique results in a high quality of generated speech that is
on par with state-of-the-art vocoders.
