An audio-visual corpus has been collected to support the use of common material in speech perception and automatic speech recognition studies. The corpus consists of high-quality audio and video recordings of 1000 sentences spoken by each of 34 talkers. Sentences are simple, syntactically identical phrases such as "place green at B 4 now". Intelligibility tests using the audio signals suggest that the material is easily identifiable in quiet and low levels of stationary noise. The annotated corpus is available on the web for research use.

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A new form of augmentative and alternative communication (AAC) device for people with severe speech impairment-the voice-input voice-output communication aid (VIVOCA)-is described. The VIVOCA recognizes the disordered speech of the user and builds messages, which are converted into synthetic speech. System development was carried out employing user-centered design and development methods, which identified and refined key requirements for the device. A novel methodology for building small vocabulary, speaker-dependent automatic speech recognizers with reduced amounts of training data, was applied. Experiments showed that this method is successful in generating good recognition performance (mean accuracy 96%) on highly disordered speech, even when recognition perplexity is increased. The selected message-building technique traded off various factors including speed of message construction and range of available message outputs. The VIVOCA was evaluated in a field trial by individuals with moderate to severe dysarthria and confirmed that they can make use of the device to produce intelligible speech output from disordered speech input. The trial highlighted some issues which limit the performance and usability of the device when applied in real usage situations, with mean recognition accuracy of 67% in these circumstances. These limitations will be addressed in future work.

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It has been well established that those working in the sex industry are at various risks of violence and crime depending on where they sell sex and the environments in which they work. What sociological research has failed to address is how crime and safety have been affected by the dynamic changing nature of sex work given the dominance of the internet and digital technologies, including the development of new markets such as webcamming. This paper reports the most comprehensive findings on the internet-based sex market in the UK demonstrating types of crimes experienced by internet-based sex workers and the strategies of risk management that sex workers adopt, building on our article in the British Journal of Sociology in 2007. We present the concept of 'blended safety repertoires' to explain how sex workers, particularly independent escorts, are using a range of traditional techniques alongside digitally enabled strategies to keep themselves safe. We contribute a deeper understanding of why sex workers who work indoors rarely report crimes to the police, reflecting the dilemmas experienced. Our findings highlight how legal and policy changes which seek to ban online adult services advertising and sex work related content within online spaces would have direct impact on the safety strategies online sex workers employ and would further undermine their safety. These findings occur in a context where aspects of sex work are quasi-criminalized through the brothel keeping legislation. We conclude that the legal and policy failure to recognize sex work as a form of employment, contributes to the stigmatization of sex work and prevents individuals working together. Current UK policy disallows a framework for employment laws and health and safety standards to regulate sex work, leaving sex workers in the shadow economy, their safety at risk in a quasi-legal system. In light of the strong evidence that the internet makes sex work safer, we argue that decriminalisation as a rights based model of regulation is most appropriate.

Dysarthria is a neurological speech disorder, which exhibits multi-fold disturbances in the speech production system of an individual and can have a detrimental effect on the speech output. In addition to the data sparseness problems, dysarthric speech is characterised by inconsistencies in the acoustic space making it extremely challenging to model. This paper investigates a variety of baseline speaker independent (SI) systems and its suitability for adaptation. The study also explores the usefulness of speaker adaptive training (SAT) for implicitly annihilating inter-speaker variations in a dysarthric corpus. The paper implements a hybrid MLLR-MAP based approach to adapt the SI and SAT systems. ALL the results reported uses UA-SPEECH dysarthric data. Our best adapted systems gave a significant absolute gain of 11.05% (20.42% relative) over the last published best result in the literature. A statistical analysis performed across various systems and its specific implementation in modelling different dysarthric severity sub-groups, showed that, SAT-adapted systems were more applicable to handle disfluencies of more severe speech and SI systems prepared from typical speech were more apt for modelling speech with low level of severity.

The STARDUST project developed robust computer speech recognizers for use by eight people with severe dysarthria and concomitant physical disability to access assistive technologies. Independent computer speech recognizers trained with normal speech are of limited functional use by those with severe dysarthria due to limited and inconsistent proximity to "normal" articulatory patterns. Severe dysarthric output may also be characterized by a small mass of distinguishable phonetic tokens making the acoustic differentiation of target words difficult. Speaker dependent computer speech recognition using Hidden Markov Models was achieved by the identification of robust phonetic elements within the individual speaker output patterns. A new system of speech training using computer generated visual and auditory feedback reduced the inconsistent production of key phonetic tokens over time.