Photographs of unfamiliar speaking faces were matched by normal right-handed subjects on the basis of perceived mouth-shape (i.e. visible speech sound) across different face-views. A clear left-hemisphere (RVF) processing advantage emerged, which was absent when the task was that of identity matching. In contrast to earlier proposals, the extraction of lip-shape from face photographs may be bet...

In this paper we present the lip-reading experiments with different sets of the features extracted from the video sequence. In our experiments we use a simple color based filtering techniques to extract the feature vectors from the incoming video signal. Some of those features are directly related to the geometrical properties of the lips (their position and visible thickness). Other features r...

This paper describes a model-based method for detecting lip region from image sequences. Our approach is by Sampled Active Contour Model (S-ACM). The original S-ACM has the problem which can’t expand. To overcome this problem, we propose the elastic S-ACM. Moreover, based on the extracted lip contour, the effective delta radius features are fed to the word HMM. We recorded ten words that uses f...

The features of human lip motion from video clips are extracted by three unsupervised learning algorithms, i.e., Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Non-negative Matrix Factorization (NMF). Since the human perception of facial motion goes through two different pathways, i.e., the lateral fusifom gyrus for the invariant aspects and the superior temporal ...

In machine lip-reading, which is identification of speech from visual-only information, there is evidence to show that visual speech is highly dependent upon the speaker [1]. Here, we use a phoneme-clustering method to form new phoneme-to-viseme maps for both individual and multiple speakers. We use these maps to examine how similarly speakers talk visually. We conclude that broadly speaking, s...

This paper proposes a lip reading technique for speech recognition by using motion estimation analysis. Motion estimation is done for lip movement image sequences representing speech. In this methodology, the motion estimation is computed without extracting the speaker’s lip contours and location. This leads to obtaining robust visual features for lip movements representing utterances. Our meth...

Consumer devices used in the home are capable of collecting ever more information from users, including audio and video. The Microsoft Kinect is particularly well-designed for tracking user speech and motion. In this paper, we explore the ability of current models of the Kinect to support use as an automatic speech recognizer (ASR). Lip reading is known to be difficult due to the many possible ...