The research work presented in this thesis introduces novel approaches for both visual region of interest extraction and visual feature extraction for use in audio-visual automatic speech recognition. In particular, the speaker‘s movement that occurs during speech is used to isolate the mouth region in video sequences and motionbased features obtained from this region are used to provide new visual features for audio-visual automatic speech recognition. The mouth region extraction approach proposed in this work is shown to give superior performance compared with existing colour-based lip segmentation methods. The new features are obtained from three separate representations of motion in the region of interest, namely the difference in luminance between successive images, block matching based motion vectors and optical flow. The new visual features are found to improve visual-only and audiovisual speech recognition performance when compared with the commonly-used appearance feature-based methods. In addition, a novel approach is proposed for visual feature extraction from either the discrete cosine transform or discrete wavelet transform representations of the mouth region of the speaker. In this work, the image transform is explored from a new viewpoint of data discrimination; in contrast to the more conventional data preservation viewpoint. The main findings of this work are that audio-visual automatic speech recognition systems using the new features extracted from the frequency bands selected according to their discriminatory abilities generally outperform those using features designed for data preservation. To establish the noise robustness of the new features proposed in this work, their performance has been studied in presence of a range of different types of noise and at various signal-to-noise ratios. In these experiments, the audio-visual automatic speech recognition systems based on the new approaches were found to give superior performance both to audio-visual systems using appearance based features and to audio-only speech recognition systems.

Visual information from a speaker's mouth region is known to improve automatic speech recognition (ASR) robustness, especially in the presence of acoustic noise. Currently, the vast majority of audio-visual ASR (AVASR) studies assume frontal images of the speaker's face, which is a rather restrictive human-computer interaction (HCI) scenario. The lack of research into AVASR across multiple views has been dictated by the lack of large corpora that contains varying pose/viewpoint speech data. Recently, research has concentrated on recognising human be- haviours within &quotmeeting " or &quotlecture " type scenarios via &quotsmart-rooms ". This has resulted in the collection of audio-visual speech data which allows for the recognition of visual speech from both frontal and non-frontal views to occur. Using this data, the main focus of this thesis was to investigate and develop various methods within the confines of a lipreading system which can recognise visual speech across multiple views. This reseach constitutes the first published work within the field which looks at this particular aspect of AVASR. The task of recognising visual speech from non-frontal views (i.e. profile) is in principle very similar to that of frontal views, requiring the lipreading system to initially locate and track the mouth region and subsequently extract visual features. However, this task is far more complicated than the frontal case, because the facial features required to locate and track the mouth lie in a much more limited spatial plane. Nevertheless, accurate mouth region tracking can be achieved by employing techniques similar to frontal facial feature localisation. Once the mouth region has been extracted, the same visual feature extraction process can take place to the frontal view. A novel contribution of this thesis, is to quantify the degradation in lipreading performance between the frontal and profile views. In addition to this, novel patch-based analysis of the various views is conducted, and as a result a novel multi-stream patch-based representation is formulated. Having a lipreading system which can recognise visual speech from both frontal and profile views is a novel contribution to the field of AVASR. How- ever, given both the frontal and profile viewpoints, this begs the question, is there any benefit of having the additional viewpoint? Another major contribution of this thesis, is an exploration of a novel multi-view lipreading system. This system shows that there does exist complimentary information in the additional viewpoint (possibly that of lip protrusion), with superior performance achieved in the multi-view system compared to the frontal-only system. Even though having a multi-view lipreading system which can recognise visual speech from both front and profile views is very beneficial, it can hardly considered to be realistic, as each particular viewpoint is dedicated to a single pose (i.e. front or profile). In an effort to make the lipreading system more realistic, a unified system based on a single camera was developed which enables a lipreading system to recognise visual speech from both frontal and profile poses. This is called pose-invariant lipreading. Pose-invariant lipreading can be performed on either stationary or continuous tasks. Methods which effectively normalise the various poses into a single pose were investigated for the stationary scenario and in another contribution of this thesis, an algorithm based on regularised linear regression was employed to project all the visual speech features into a uniform pose. This particular method is shown to be beneficial when the lipreading system was biased towards the dominant pose (i.e. frontal). The final contribution of this thesis is the formulation of a continuous pose-invariant lipreading system which contains a pose-estimator at the start of the visual front-end. This system highlights the complexity of developing such a system, as introducing more flexibility within the lipreading system invariability means the introduction of more error. All the works contained in this thesis present novel and innovative contributions to the field of AVASR, and hopefully this will aid in the future deployment of an AVASR system in realistic scenarios.