Comparison of Image Transform-Based Features for Visual Speech Recognition in Clean and Corrupted Videos

We present results of a study into the performance of a variety of different image transform-based feature types for speaker-independent visual speech recognition of isolated digits. This includes the first reported use of features extracted using a discrete curvelet transform. The study will show a comparison of some methods for selecting features of each feature type and show the relative benefits of both static and dynamic visual features. The performance of the features will be tested on both clean video data and also video data corrupted in a variety of ways to assess each feature type's robustness to potential real-world conditions. One of the test conditions involves a novel form of video corruption we call jitter which simulates camera and/or head movement during recording.

Beyond the Sign. Henri Meschonnic's Poetics of Rhythm and Continuum. Towards an Anthropological Theory of Language

Basing the conception of language on the sign represents also an obstacle to the awareness of certain elements of human life, especially to a full understanding of what language or art do, Henri Meschonnic’s poetics of the continuum and of rhythm criticizes the sign based on Benveniste’s terms of rhythm and discourse, developing an anthropology of language. Rhythm, for Meschonnic, is no formal metrical but a semantic principle, each time unique and unforeseeable. As for Humboldt, his starting point is not the word but the ensemble of speech; language is not ergon but energeia. The poem then is not a literary form but a process of transformation that Meschonnic defines as the invention of a form of life by a form of language and vice versa. Thus a poem is a way of thinking and rhythm is form in movement. The particular subject of art and literature is consequently not the author but a process of subjectivation – this is the contrary of the conception of the sign. By demonstrating the limits of the sign, Meschonnic’s poetics attempts to thematize the intelligibility of presence. Art and literature raise our awareness of this element of human life we cannot grasp conceptually. This poetical thinking is a necessary counterforce against all institutionalization.

Political Liberalism, Free Speech and Public Reason

In this paper, I critically assess John Rawls' repeated claim that the duty of civility is only a moral duty and should not be enforced by law. In the first part of the paper, I examine and reject the view that Rawls' position may be due to the practical difficulties that the legal enforcement of the duty of civility might entail. I thus claim that Rawls' position must be driven by deeper normative reasons grounded in a conception of free speech. In the second part of the paper, I therefore examine various arguments for free speech and critically assess whether they are consistent with Rawls' political liberalism. I first focus on the arguments from truth and self-fulfilment. Both arguments, I argue, rely on comprehensive doctrines and therefore cannot provide a freestanding political justification for free speech. Freedom of speech, I claim, can be justified instead on the basis of Rawls' political conception of the person and of the two moral powers. However, Rawls' wide view of public reason already allows scope for the kind of free speech necessary for the exercise of the two moral powers and therefore cannot explain Rawls' opposition to the legal enforcement of the duty of civility. Such opposition, I claim, can only be explained on the basis of a defence of unconstrained freedom of speech grounded in the ideas of democracy and political legitimacy. Yet, I conclude, while public reason and the duty of civility are essential to political liberalism, unconstrained freedom of speech is not. Rawls and political liberals could therefore renounce unconstrained freedom of speech, and endorse the legal enforcement of the duty of civility, while remaining faithful to political liberalism.

Robust Speaker Recognition in Noisy Conditions

This paper investigates the problem of speaker identi-fication and verification in noisy conditions, assuming that speechsignals are corrupted by environmental noise, but knowledgeabout the noise characteristics is not available. This research ismotivated in part by the potential application of speaker recog-nition technologies on handheld devices or the Internet. Whilethe technologies promise an additional biometric layer of securityto protect the user, the practical implementation of such systemsfaces many challenges. One of these is environmental noise. Due tothe mobile nature of such systems, the noise sources can be highlytime-varying and potentially unknown. This raises the require-ment for noise robustness in the absence of information about thenoise. This paper describes a method that combines multicondi-tion model training and missing-feature theory to model noisewith unknown temporal-spectral characteristics. Multiconditiontraining is conducted using simulated noisy data with limitednoise variation, providing a “coarse” compensation for the noise,and missing-feature theory is applied to refine the compensationby ignoring noise variation outside the given training conditions,thereby reducing the training and testing mismatch. This paperis focused on several issues relating to the implementation of thenew model for real-world applications. These include the gener-ation of multicondition training data to model noisy speech, thecombination of different training data to optimize the recognitionperformance, and the reduction of the model’s complexity. Thenew algorithm was tested using two databases with simulated andrealistic noisy speech data. The first database is a redevelopmentof the TIMIT database by rerecording the data in the presence ofvarious noise types, used to test the model for speaker identifica-tion with a focus on the varieties of noise. The second database isa handheld-device database collected in realistic noisy conditions,used to further validate the model for real-world speaker verifica-tion. The new model is compared to baseline systems and is foundto achieve lower error rates.

Emotional speech: towards a new generation of databases

Research on speech and emotion is moving from a period of exploratory research into one where there is a prospect of substantial applications, notably in human-computer interaction. Progress in the area relies heavily on the development of appropriate databases. This paper addresses the issues that need to be considered in developing databases of emotional speech, and shows how the challenge of developing apropriate databases is being addressed in three major recent projects - the Belfast project, the Reading-Leeds project and the CREST-ESP project. From these and other studies the paper draws together the tools and methods that have been developed, addresses the problems that arise and indicates the future directions for the development of emotional speech databases.

Speech Recognition with unknown partial feature corruption - a review of the union model

This paper provides a summary of our studies on robust speech recognition based on a new statistical approach – the probabilistic union model. We consider speech recognition given that part of the acoustic features may be corrupted by noise. The union model is a method for basing the recognition on the clean part of the features, thereby reducing the effect of the noise on recognition. To this end, the union model is similar to the missing feature method. However, the two methods achieve this end through different routes. The missing feature method usually requires the identity of the noisy data for noise removal, while the union model combines the local features based on the union of random events, to reduce the dependence of the model on information about the noise. We previously investigated the applications of the union model to speech recognition involving unknown partial corruption in frequency band, in time duration, and in feature streams. Additionally, a combination of the union model with conventional noise-reduction techniques was studied, as a means of dealing with a mixture of known or trainable noise and unknown unexpected noise. In this paper, a unified review, in the context of dealing with unknown partial feature corruption, is provided into each of these applications, giving the appropriate theory and implementation algorithms, along with an experimental evaluation.