Anagrams are used widely in psychological research. However, generating a range of strings with the same letter content is an inherently difficult and time-consuming task for humans, and current computer-based anagram generators do not provide the controls necessary for psychological research. In this article, we present a computational algorithm that overcomes these problems. Specifically, the algorithm processes automatically each word in a user-defined source vocabulary and outputs, for each word, all possible anagrams that exist as words (or as nonwords, if required) as defined by the same source vocabulary. Moreover, we show how the output of the algorithm can be filtered to produce anagrams within specific user-defined orthographic parameters. For example, the anagrams produced can be filtered to produce words that share, with each other or with other words in the source vocabulary, letters in only certain positions. Finally, we provide free access to the complete Windows-based program and source code containing these facilities for anagram generation.
Research has shown that the sight of a speaker's face can dramatically improve the intelligibility of speech. The addition of a video stream to telecommunications systems is, therefore, a highly desirable goal. Extending the content-based coding paradigm of MPEG-4, we present a novel method of reducing the amount of data required to represent video images of human speakers. This method is based around the mirroring of vertical halves of the speaker's face. We report an experiment that compared video of synthesized speaking faces (constructed by mirroring hemifaces) with that of "normal" (un-manipulated) speaking faces. The results of the experiment show that the synthesized faces provided the same benefits to speech perception as the normal faces. Development of this technique as an image-coding technique for audiovisual telecommunication applications is discussed.
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