In this paper, we discuss the choice of specific sounds to use in an audio Hl'ML interface, based on our previous research into developing principles for sound choice, called the AHA framework. AHA can be used along with the cons&ration of issues related to the target audience such as user tasks, goals, and interests to choose specific sounds for an interface. We describe two scenarios of potential users and interfaces that would seem to be appropriate for them.
We describe an architecture for spoken dialogue interfaces to semi-autonomous systems that transforms speech signals through successive representations of linguistic, dialogue, and domain knowledge. Each step produces an output, and a meta-output describing the transformation, with an executable program in a simple scripting language as the final result. The output/meta-output distinction permits perspicuous treatment of diverse tasks such as resolving pronouns, correcting user misconceptions, and optimizing scripts.
Systems now exist which are able to compile unification grammars into language models that can be included in a speech recognizer, but it is so far unclear whether non-trivial linguistically principled grammars can be used for this purpose. We describe a series of experiments which investigate the question empirically, by incrementally constructing a grammar and discovering what problems emerge when successively larger versions are compiled into finite state graph representations and used as language models for a medium-vocabulary recognition task.
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