Automatic visual speech recognition is an interesting problem in pattern recognition especially when audio data is noisy or not readily available. It is also a very challenging task mainly because of the lower amount of information in the visual articulations compared to the audible utterance. In this work, principle component analysis is applied to the image patches — extracted from the video data — to learn the weights of a two-stage convolutional network. Block histograms are then extracted as the unsupervised learning features. These features are employed to learn a recurrent neural network with a set of long short-term memory cells to obtain spatiotemporal features. Finally, the obtained features are used in a tandem GMM-HMM system for speech recognition. Our results show that the proposed method has outperformed the baseline techniques applied to the OuluVS2 audiovisual database for phrase recognition with the frontal view cross-validation and testing sentence correctness reaching 79 and 73 , respectively, as compared to the baseline of 74 on cross-validation.
Visual speech recognition requires a series of steps to process the video and extract relevant features. First of all, a region of interest (ROI) around the mouth, which contains the largest amount of information about the utterance, has to be extracted @cite_17 . This can be done by hand or with the help of a face tracker. The latter is more common nowadays even though manual corrections are still sometimes applied. The ROI is later used to extract the features.
@cite_17
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We have made significant progress in automatic speech recognition (ASR) for well-defined applications like dictation and medium vocabulary transaction processing tasks in relatively controlled environments. However, ASR performance has yet to reach the level required for speech to become a truly pervasive user interface. Indeed, even in “clean” acoustic environments, and for a variety of tasks, state of the art ASR system performance lags human speech perception by up to an order of magnitude (Lippmann, 1997). In addition, current systems are quite sensitive to channel, environment, and style of speech variations. A number of techniques for improving ASR robustness have met limited success in severely degraded environments, mismatched to system training (Ghitza, 1986; , 1989; Juang, 1991; , 1993; Hermansky and Morgan, 1994; Neti, 1994; Gales, 1997; , 2001). Clearly, novel, non-traditional approaches, that use orthogonal sources of information to the acoustic input, are needed to achieve ASR performance closer to the human speech perception level, and robust enough to be deployable in field applications. Visual speech is the most promising source of additional speech information, and it is obviously not affected by the acoustic environment and noise.