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Language learning is a multimodal endeavor; to improve their pronunciation in a new language, learners access not only auditory information about speech sounds and patterns, but also visual information about articulatory movements and processes. With the development of new technologies in computer-assisted pronunciation training (CAPT) comes new possibilities for delivering feedback in both auditory and visual modalities. The present paper surveys the literature on computer-assisted visual articulation feedback, including direct feedback that provides visual models of articulation and indirect feedback that uses visualized acoustic information as a means to inform articulation instruction. Our focus is explicitly on segmental features rather than suprasegmental ones, with visual feedback conceived of as providing visualizations of articulatory configurations, movements, and processes. In addition to discussing types of visual articulation feedback, we also consider the criteria for effective delivery of feedback, and methods of evaluation.

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Nominalizations in Blackfoot can be formed of full clauses, and depending on the properties of the clause from which the nominalization is formed, the referent of the nominalization varies. In this paper I describe the patterns of reference assignment in Blackfoot nominalizations, and develop an analysis to account for the various patterns. I demonstrate that clausal nominalizations partition according to clause type: in matrix clause nominalizations the referent is a grammatical argument (subject or object), but in subordinate clauses it is not (thematic object or oblique). I propose that reference assignment in clausal nominalizations is achieved via an agreement relation with an N feature, and that the two types of nominalizations differ with respect to where this N feature is realized.