Automatic Lip Reading in the Dutch Language Using Active Appearance Models on High Speed Recordings

This paper presents our work on lip reading in the Dutch language. The results are based on a new data corpus recorded at 100Hz in our group. The NDUTAVSC corpus is to date the largest corpus build for lip reading in Dutch. For parameterising the input data we use Active Appearance Models. Based on the results of AAM we define a set of high level geometric features which are used for training recognizer systems for different recognition tasks, such as fixed length digits strings, random length letters strings, random word sequences, fixed topic continuous speech and random continuous speech. We show that our approach gives great improvements compared to previous results. We also investigate the influence of the high speed recordings on the performance of the recognition. We show that in the case of high speech rate the use of higher speed recordings is compulsory.