  Speech is considered as a multi-modal process where hearing and vision are
two fundamentals pillars. In fact, several studies have demonstrated that the
robustness of Automatic Speech Recognition systems can be improved when audio
and visual cues are combined to represent the nature of speech. In addition,
Visual Speech Recognition, an open research problem whose purpose is to
interpret speech by reading the lips of the speaker, has been a focus of
interest in the last decades. Nevertheless, in order to estimate these systems
in the currently Deep Learning era, large-scale databases are required. On the
other hand, while most of these databases are dedicated to English, other
languages lack sufficient resources. Thus, this paper presents a
semi-automatically annotated audiovisual database to deal with unconstrained
natural Spanish, providing 13 hours of data extracted from Spanish television.
Furthermore, baseline results for both speaker-dependent and
speaker-independent scenarios are reported using Hidden Markov Models, a
traditional paradigm that has been widely used in the field of Speech
Technologies.
