Visual units and confusion modelling for automatic lip-reading

Automatic lip-reading (ALR) is a challenging task because the visual speech signal is known to be missing some important information, such as voicing. We propose an approach to ALR that acknowledges that this information is missing but assumes that it is substituted or deleted in a systematic way that can be modelled. We describe a system that learns such a model and then incorporates it into decoding, which is realised as a cascade of weighted finite-state transducers. Our results show a small but statistically significant improvement in recognition accuracy. We also investigate the issue of suitable visual units for ALR, and show that visemes are sub-optimal, not but because they introduce lexical ambiguity, but because the reduction in modelling units entailed by their use reduces accuracy

Lipreading difficulty during audiovisual integration

Audiovisual integration ability for word-level stimuli was assessed using two talkers, one easy to lipread and the other hard to lipread. No significant effect for integration ability was found for the two talkers

Statistical Lip-Appearance Models Trained Automatically Using Audio Information

We aim at modeling the appearance of the lower face region to assist visual feature extraction for audio-visual speech processing applications. In this paper, we present a neural network based statistical appearance model of the lips which classifies pixels as belonging to the lips, skin, or inner mouth classes. This model requires labeled examples to be trained, and we propose to label images automatically by employing a lip-shape model and a red-hue energy function. To improve the performance of lip-tracking, we propose to use blue marked-up image sequences of the same subject uttering the identical sentences as natural nonmarked-up ones. The easily extracted lip shapes from blue images are then mapped to the natural ones using acoustic information. The lip-shape estimates obtained simplify lip-tracking on the natural images, as they reduce the parameter space dimensionality in the red-hue energy minimization, thus yielding better contour shape and location estimates. We applied the proposed method to a small audio-visual database of three subjects, achieving errors in pixel classification around 6%, compared to 3% for hand-placed contours and 20% for filtered red-hue

Multimodal Based Audio-Visual Speech Recognition for Hard-of-Hearing: State of the Art Techniques and Challenges

Multimodal Integration (MI) is the study of merging the knowledge acquired by the nervous system using sensory modalities such as speech, vision, touch, and gesture. The applications of MI expand over the areas of Audio-Visual Speech Recognition (AVSR), Sign Language Recognition (SLR), Emotion Recognition (ER), Bio Metrics Applications (BMA), Affect Recognition (AR), Multimedia Retrieval (MR), etc. The fusion of modalities such as hand gestures- facial, lip- hand position, etc., are mainly used sensory modalities for the development of hearing-impaired multimodal systems. This paper encapsulates an overview of multimodal systems available within literature towards hearing impaired studies. This paper also discusses some of the studies related to hearing-impaired acoustic analysis. It is observed that very less algorithms have been developed for hearing impaired AVSR as compared to normal hearing. Thus, the study of audio-visual based speech recognition systems for the hearing impaired is highly demanded for the people who are trying to communicate with natively speaking languages.  This paper also highlights the state-of-the-art techniques in AVSR and the challenges faced by the researchers for the development of AVSR systems