7 research outputs found
Asymptotic Generalization Bound of Fisher's Linear Discriminant Analysis
Fisher's linear discriminant analysis (FLDA) is an important dimension
reduction method in statistical pattern recognition. It has been shown that
FLDA is asymptotically Bayes optimal under the homoscedastic Gaussian
assumption. However, this classical result has the following two major
limitations: 1) it holds only for a fixed dimensionality , and thus does not
apply when and the training sample size are proportionally large; 2) it
does not provide a quantitative description on how the generalization ability
of FLDA is affected by and . In this paper, we present an asymptotic
generalization analysis of FLDA based on random matrix theory, in a setting
where both and increase and . The
obtained lower bound of the generalization discrimination power overcomes both
limitations of the classical result, i.e., it is applicable when and
are proportionally large and provides a quantitative description of the
generalization ability of FLDA in terms of the ratio and the
population discrimination power. Besides, the discrimination power bound also
leads to an upper bound on the generalization error of binary-classification
with FLDA
A motion-based approach for audio-visual automatic speech recognition
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 speech recognition:from traditional to deep learning frameworks
Speech is the most natural means of communication for humans. Therefore, since the beginning of computers it has been a goal to interact with machines via speech. While there have been gradual improvements in this field over the decades, and with recent drastic progress more and more commercial software is available that allow voice commands, there are still many ways in which it can be improved.
One way to do this is with visual speech information, more specifically, the visible articulations of the mouth. Based on the information contained in these articulations, visual speech recognition (VSR) transcribes an utterance from a video sequence. It thus helps extend speech recognition from audio-only to other scenarios such as silent or whispered speech (e.g.\ in cybersecurity), mouthings in sign language, as an additional modality in noisy audio scenarios for audio-visual automatic speech recognition, to better understand speech production and disorders, or by itself for human machine interaction and as a transcription method.
In this thesis, we present and compare different ways to build systems for VSR: We start with the traditional hidden Markov models that have been used in the field for decades, especially in combination with handcrafted features. These are compared to models taking into account recent developments in the fields of computer vision and speech recognition through deep learning. While their superior performance is confirmed, certain limitations with respect to computing power for these systems are also discussed.
This thesis also addresses multi-view processing and fusion, which is an important topic for many current applications. This is due to the fact that a single camera view often cannot provide enough flexibility with speakers moving in front of the camera. Technology companies are willing to integrate more cameras into their products, such as cars and mobile devices, due to lower hardware cost for both cameras and processing units, as well as the availability of higher processing power and high performance algorithms. Multi-camera and multi-view solutions are thus becoming more common, which means that algorithms can benefit from taking these into account. In this work we propose several methods of fusing the views of multiple cameras to improve the overall results.
We can show that both, relying on deep learning-based approaches for feature extraction and sequence modelling, as well as taking into account the complementary information contained in several views, improves performance considerably. To further improve the results, it would be necessary to move from data recorded in a lab environment, to multi-view data in realistic scenarios. Furthermore, the findings and models could be transferred to other domains such as audio-visual speech recognition or the study of speech production and disorders
A motion based approach for audio-visual automatic speech recognition
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.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Linear Discriminant Analysis For Speechreading
This paper investigates the use of Fisher-Rao linear discriminant analysis (LDA) as a means of visual feature extraction for hidden Markov model based automatic speechreading. For every video frame, a three-dimensional region of interest containing the speaker's mouth over a sequence of adjacent frames is lexicographically arranged into a data vector. Suchvectors are then projected onto the space of the most discriminant "eigensequences", estimated by means of LDA on a training set of image sequence vectors, labeled from a set of a-priori chosen classes. The resulting projections, as well as their first and second derivatives over time, are used as features for automatic speechreading. The proposed method is applied to single-speaker, multi-speaker, and speaker-independent visual-only recognition tasks, consistently outperforming principal component analysis and discrete wavelet transform based visual features. Specific issues relevant to LDA are also discussed, namely, class selection, aut..