Biomedical subjects

C L D'Autrechy

Publications and source records attributed to C L D'Autrechy.

3 recordsLinked to original sources

PubMed1994

Functional pronunciation units in English words.

Two distinct factors limit the orthographic regularity of English words: (a) Most characters can correspond to several different sounds and (b) characters can either stand alone or be combined in various ways for pronunciation as a single phoneme. This study addresses the second of these issues through the analysis of a large corpus of English words. Data are presented describing the frequency that each character (or character cluster) functioned in the corpus as a correspondent of a single phoneme rather than being combined with other characters (or decomposed). Examples are provided regarding potential applications of these data in the construction of stimulus materials for cognitive studies, in neuropsychological investigations of dyslexia, and in computational models of word naming.

Humans↗

PubMed1991

Modelling acquired dyslexia: a software tool for developing grapheme-phoneme correspondences.

In extending a computer model of acquired dyslexia, it has become necessary to develop a way to group printed characters in a word so that the character groups essentially have a one-to-one correspondence with the word's phonemes (speech sounds). This requires deriving a set of correspondences (legal character groupings, legal associations of character groups with phonemes, etc.) that yield a single grouping or "segmentation" of characters when applied to any English word. To facilitate and partially automate this task, a segmentation program has been developed that uses an interchangeable set of correspondences. The program segments words according to these correspondences and tabulates their success over large sets of words. The program has been used successfully to segment a 20,000 word corpus, demonstrating that this approach can be used effectively and efficiently.

Algorithms↗

PubMedDate not supplied

Maryland MIRRORS/II: a connectionist model simulator.

Developing and evaluating connectionist models (also called neural models) is a difficult and time-consuming task. To address this issue, we designed a software system called Maryland MIRRORS/II for the construction of connectionist models in biomedicine and other fields. Maryland MIRRORS/II is distinguished from previous and current related systems by its support of a high-level nonprocedural language, a general-purpose event-handling mechanism, and an indexed library of system resources. These features make Maryland MIRRORS/II a convenient software tool for use in biomedicine. This paper describes Maryland MIRRORS/II and provides a simple example in which it uses error back propagation learning to select the appropriate treatment for a given set of manifestations.

Computer Simulation↗