Probed serial recall in Williams syndrome: Lexical influences on phonological short-term memory

Williams syndrome is a genetic disorder that, it has been claimed, results in an unusual pattern of linguistic strengths and weaknesses. The current study investigated the hypothesis that there is a reduced influence of lexical knowledge on phonological short-term memory in Williams syndrome. Fourteen children with Williams syndrome and 2 vocabulary la matched control groups, 20 typically developing children and 13 children with learning difficulties, were tested on 2 probed serial-recall tasks. On the basis of previous findings, it was predicted that children with Williams syndrome would demonstrate (a) a reduced effect of lexicality on the recall of list items, (b) relatively poorer recall of list items compared with recall of serial order, and (c) a reduced tendency to produce lexicalization errors in the recall of nonwords. in fact, none of these predictions were supported. Alternative explanations for previous findings and implications for accounts of language development in Williams syndrome are discussed.

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.