  Automatic lipreading has major potential impact for speech recognition,
supplementing and complementing the acoustic modality. Most attempts at
lipreading have been performed on small vocabulary tasks, due to a shortfall of
appropriate audio-visual datasets. In this work we use the publicly available
TCD-TIMIT database, designed for large vocabulary continuous audio-visual
speech recognition. We compare the viseme recognition performance of the most
widely used features for lipreading, Discrete Cosine Transform (DCT) and Active
Appearance Models (AAM), in a traditional Hidden Markov Model (HMM) framework.
We also exploit recent advances in AAM fitting. We found the DCT to outperform
AAM by more than 6% for a viseme recognition task with 56 speakers. The overall
accuracy of the DCT is quite low (32-34%). We conclude that a fundamental
rethink of the modelling of visual features may be needed for this task.
