noise-related loss; with both of these forms of hearing loss, the high-frequency sounds are lost first. Since many of the consonants in speech are high-frequency sounds, speech becomes distorted. Hearing aids help but may not cure this. Lipreading classes have been shown to be of benefit in UK studies commissioned by the Action on Hearing Loss charity (2012). Trainers recognise that lipreading is an inexact art. Students are taught to watch the lips, tongue and jaw movements, to follow the stress and rhythm of language, to use their residual hearing, with or without hearing aids, to watch expression and body language, and to use their ability to reason and deduce. They are taught the lipreaders' alphabet, groups of sounds that look alike on the lips (visemes) like p, b, m, or f, v. The aim is to get the gist, so as to have the confidence to join in conversation and avoid the damaging social isolation that often accompanies hearing loss. Lipreading classes are recommended for anyone who struggles to hear in noise, and help to adjust to hearing loss. ATLA(Association for Teaching Lipreading to Adults) is the UK professional association for qualified lipreading tutors. Tests Most tests of lipreading were devised to measure individual differences in performing specific speech-processing tasks and to detect changes in performance following training. Lipreading tests have been used with relatively small groups in experimental settings, or as clinical indicators with individual patients and clients. That is, lipreading tests to date have limited validity as markers of lipreading skill in the general population. Lipreading and lip-speaking by machine Automated lip-reading has been a topic of interest in computational engineering, as well as in science fiction movies. The computational engineer Steve Omohundro, among others, pioneered its development. In facial animation, the aim is to generate realistic facial actions, especially mouth movements, that simulate human speech actions. Computer algorithms to deform or manipulate images of faces can be driven by heard or written language. Systems may be based on detailed models derived from facial movements (motion capture); on anatomical modelling of actions of the jaw, mouth and tongue; or on mapping of known viseme- phoneme properties. Facial animation has been used in speechreading training (demonstrating how different sounds 'look'). These systems are a subset of speech synthesis modelling which aim to deliver reliable 'text-to-(seen)-speech' outputs. A complementary aim—the reverse of making faces move in speech—is to develop