The study of musical expressivity is an active field in sound and music computing. The research interest comes from different motivations: to understand or model music expressivity; to identify the expressive resources that characterize an instrument, musical genre, or performer; or to build synthesis systems able to play expressively. In this paper, we present a system that focuses on the stud...

BACKGROUND Synthetic phonics is the widely accepted approach for teaching reading in English: Children are taught to sound out the letters in a word then blend these sounds together. AIMS We compared the impact of two synthetic phonics programmes on early reading. SAMPLE Children received Letters and Sounds (L&S; 7 schools) which teaches multiple letter-sound mappings or Early Reading Resea...

The authors review the neuropsychology, brain bases, and genetics of three related disorders of language development: reading disability, or developmental dyslexia (RD); language impairment (LI); and speech sound disorder (SSD). Over the past three decades, cognitive analysis has demonstrated that the reading difficulties of most children who have RD result from phonologic impairments (difficul...

Developmental dyslexia affects up to 10 per cent of the population and it is important to understand its causes. It is widely assumed that phonological deficits, that is, deficits in how words are sounded out, cause the reading difficulties in dyslexia. However, there is emerging evidence that phonological problems and the reading impairment both arise from poor visual (i.e., orthographic) codi...

PURPOSE In this study, the authors evaluated literacy outcome in children with histories of speech sound disorder (SSD) who were characterized along 2 dimensions: broader language function and persistence of SSD. In previous studies, authors have demonstrated that each dimension relates to literacy but have not disentangled their effects. Methods Two groups of children (86 SSD and 37 controls) ...

A voice to facial animation conversion system is presented in this paper. In particular the temporal structure of the multimodal speech is discussed. Mutual information and neural network training is used to estimate the optimal temporal scope for audio to video conversion.