  Thanks to the rise of deep learning and the availability of large-scale
audio-visual databases, recent advances have been achieved in Visual Speech
Recognition (VSR). Similar to other speech processing tasks, these end-to-end
VSR systems are usually based on encoder-decoder architectures. While encoders
are somewhat general, multiple decoding approaches have been explored, such as
the conventional hybrid model based on Deep Neural Networks combined with
Hidden Markov Models (DNN-HMM) or the Connectionist Temporal Classification
(CTC) paradigm. However, there are languages and tasks in which data is scarce,
and in this situation, there is not a clear comparison between different types
of decoders. Therefore, we focused our study on how the conventional DNN-HMM
decoder and its state-of-the-art CTC/Attention counterpart behave depending on
the amount of data used for their estimation. We also analyzed to what extent
our visual speech features were able to adapt to scenarios for which they were
not explicitly trained, either considering a similar dataset or another
collected for a different language. Results showed that the conventional
paradigm reached recognition rates that improve the CTC/Attention model in
data-scarcity scenarios along with a reduced training time and fewer
parameters.
