  Content mismatch usually occurs when data from one modality is translated to
another, e.g. language learners producing mispronunciations (errors in speech)
when reading a sentence (target text) aloud. However, most existing alignment
algorithms assume that the content involved in the two modalities is perfectly
matched, thus leading to difficulty in locating such mismatch between speech
and text. In this work, we develop an unsupervised learning algorithm that can
infer the relationship between content-mismatched cross-modal sequential data,
especially for speech-text sequences. More specifically, we propose a
hierarchical Bayesian deep learning model, dubbed mismatch localization
variational autoencoder (ML-VAE), which decomposes the generative process of
the speech into hierarchically structured latent variables, indicating the
relationship between the two modalities. Training such a model is very
challenging due to the discrete latent variables with complex dependencies
involved. To address this challenge, we propose a novel and effective training
procedure that alternates between estimating the hard assignments of the
discrete latent variables over a specifically designed mismatch localization
finite-state acceptor (ML-FSA) and updating the parameters of neural networks.
In this work, we focus on the mismatch localization problem for speech and
text, and our experimental results show that ML-VAE successfully locates the
mismatch between text and speech, without the need for human annotations for
model training.
