Multimodal feature extraction and fusion for audio-visual speech recognition

Multimodal signal processing analyzes a physical phenomenon through several types of measures, or modalities. This leads to the extraction of higher-quality and more reliable information than that obtained from single-modality signals. The advantage is two-fold. First, as the modalities are usually complementary, the end-result of multimodal processing is more informative than for each of the modalities individually, which represents the first advantage. This is true in all application domains: human-machine interaction, multimodal identification or multimodal image processing. The second advantage is that, as modalities are not always reliable, it is possible, when one modality becomes corrupted, to extract the missing information from the other one. There are two essential challenges in multimodal signal processing. First, the features used from each modality need to be as relevant and as few as possible. The fact that multimodal systems have to process more than just one modality means that they can run into errors caused by the curse of dimensionality much more easily than mono-modal ones. The curse of dimensionality is a term used essentially to say that the number of equally-distributed samples required to cover a region of space grows exponentially with the dimensionality of the space. This has important implications in the classification domain, since accurate models can only be obtained if an adequate number of samples is available, and obviously this required number of samples grows with the dimensionality of the features. Dimensionality reduction is thus a necessary step in any application dealing with complex signals, and this is achieved through selection, transforms or the combination of the two. The second essential challenge is multimodal integration. Since the signals involved do not necessarily have the same data rate, range or even dimensionality, combining information coming from such different sources is not straightforward. This can be done at different levels, starting from the basic signal level by combining the signals themselves, if they are compatible, up to the highest decision level, where only the individual decisions taken based on the signals are combined. Ideally, the fusion method should allow temporal variations in the relative importance of the two streams, to account for possible changes in their quality. However, this can only be done with methods operating at a high decision level. The aim of this thesis is to offer solutions to both these challenges, in the context of audio-visual speech recognition and speaker localization. Both these applications are from the field of human-machine interaction. Audio-visual speech recognition aims to improve the accuracy of speech recognizers by augmenting the audio with information extracted from the video, more particularly, the movement of the speaker's lips. This works well especially when the audio is corrupted, leading in this case to significant gains in accuracy. Speaker localization means detecting who is the active speaker in a audio-video sequence containing several persons, something that is useful for videoconferencing and the automated annotation of meetings. These two applications are the context in which we present our solutions to both feature selection and multimodal integration. First, we show how informative features can be extracted from the visual modality, using an information-theoretic framework which gives us a quantitative measure of the relevance of individual features. We also prove that reducing redundancy between these features is important for avoiding the curse of dimensionality and improving recognition results. The methods that we present are novel in the field of audio-visual speech recognition and we found that their use leads to significant improvements compared to the state of the art. Second, we present a method of multimodal fusion at the level of intermediate decisions using a weight for each of the streams. The weights are adaptive, changing according to the estimated reliability of each stream. This makes the system tolerant to changes in the quality of either stream, and even to the temporary interruption of one of the streams. The reliability estimate is based on the entropy of the posterior probability distributions of each stream at the intermediate decision level. Our results are superior to those obtained with a state of the art method based on maximizing the same posteriors. Moreover, we analyze the effect of a constraint typically imposed on stream weights in the literature, the constraint that they should sum to one. Our results show that removing this constraint can lead to improvements in recognition accuracy. Finally, we develop a method for audio-visual speaker localization, based on the correlation between audio energy and the movement of the speaker's lips. Our method is based on a joint probability model of the audio and video which is used to build a likelihood map showing the likely positions of the speaker's mouth. We show that our novel method performs better than a similar method from the literature. In conclusion, we analyze two different challenges of multimodal signal processing for two audio-visual problems, and offer innovative approaches for solving them

Information theoretic feature extraction for audio-visual speech recognition

The problem of feature selection has been thoroughly analyzed in the context of pattern classification, with the purpose of avoiding the curse of dimensionality. However, in the context of multimodal signal processing, this problem has been studied less. Our approach to feature extraction is based on information theory, with an application on multimodal classification, in particular audio-visual speech recognition. Contrary to previous work in information theoretic feature selection applied to multimodal signals, our proposed methods penalize features for their redundancy, achieving more compact feature sets and better performance. We propose two greedy selection algorithms, one that penalizes a proportion of feature redundancy, while the other uses conditional mutual information as an evaluation measure, for the selection of visual features for audio-visual speech recognition. Our features perform better than linear discriminant analysis, the most usual transform for dimensionality reduction in the field, across a wide range of dimensionality values and combined with audio at different quality levels

SELECTING RELEVANT VISUAL FEATURES FOR SPEECHREADING

A quantitative measure of relevance is proposed for the task of constructing visual feature sets which are at the same time relevant and compact. A feature's relevance is given by the amount of information that it contains about the problem, while compactness is achieved by preventing the replication of information between features in the set. To achieve these goals, we use mutual information both for assessing relevance and measuring the redundancy between features. Our application is speechreading, that is, speech recognition performed on the video of the speaker. This is justified by the fact that the performance of audio speech recognition can be improved by augmenting the audio features with visual ones, especially when there is noise in the audio channel. We report significant improvements compared to the most commonly used method of dimensionality reduction for speechreading, linear discriminant analysis

On dynamic stream weighting for Audio-Visual Speech Recognition

The integration of audio and visual information improves speech recognition performance, specially in the presence of noise. In these circumstances it is necessary to introduce audio and visual weights to control the contribution of each modality to the recognition task. We present a method to set the value of the weights associated to each stream according to their reliability for speech recognition, allowing them to change with time and adapt to different noise and working conditions. Our dynamic weights are derived from several measures of the stream reliability, some specific to speech processing and others inherent to any classification task, and take into account the special role of silence detection in the definition of audio and visual weights. In this paper we propose a new confidence measure, compare it to existing ones and point out the importance of the correct detection of silence utterances in the definition of the weighting system. Experimental results support our main contribution: the inclusion of a voice activity detector in the weighting scheme improves speech recognition over different system architectures and confidence measures, leading to an increase in performance more relevant than any difference between the proposed confidence measures

Multimodal Speaker Localization from Omnidirectional Videos

The use of omnidirectional cameras for videoconferencing promises to simplify the hardware setup necessary for large groups of participants. We investigate the use of a multimodal speaker detection algorithm on audio-visual sequences captured with such a camera, in particular, an algorithm that uses the audio energy together with the optical flow. We analyze several types of optical flow methods to determine the one which is appropriate to the omnidirectional context

Dynamic modality weighting for multi-stream HMMs in Audio- Visual Speech Recognition

Merging decisions from different modalities is a crucial problem in Audio-Visual Speech Recognition. To solve this, state synchronous multi-stream HMMs have been proposed for their important advantage of incorporating stream reliability in their fusion scheme. This paper focuses on stream weight adaptation based on modality confidence estimators. We assume different and time-varying environment noise, as can be encountered in realistic applications, and, for this, adaptive methods are best- suited. Stream reliability is assessed directly through classifier outputs since they are not specific to either noise type or level. The influence of constraining the weights to sum to one is also discussed

Multimodal speaker localization in a probabilistic framework

A multimodal probabilistic framework is proposed for the problem of finding the active speaker in a video sequence. We localize the current speaker’s mouth in the image by using the video and the audio channels together. We propose a novel visual feature that is well-suited for the analysis of the movement of the mouth. After estimating the joint probability density of the audio and visual features, we can find the most probable location of the current speaker’s mouth in a sequence of images. The proposed method is tested on the CUAVE audio-visual database, yielding improved results, compared to other approaches from the literature. 1