Speech repairs: A parsing perspective
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Using acoustic and prosodic cues to correct Chinese speech repairs
Speech repairs introduce much noise in spoken language processing. Properly correcting speech repairs can help the speech recognizer to avoid the textual errors, and prevent the interpretation errors during the subsequent processing. Because the task of repair processing cannot defer to the latter (word segmentation, part-of-speech tagging and sentence parsing) stages, this paper employs acoust...
متن کاملWord Buffering Models for Improved Speech Repair Parsing
This paper describes a time-series model for parsing transcribed speech containing disfluencies. This model differs from previous parsers in its explicit modeling of a buffer of recent words, which allows it to recognize repairs more easily due to the frequent overlap in words between errors and their repairs. The parser implementing this model is evaluated on the standard Switchboard transcrib...
متن کاملParsing Speech Repair without Specialized Grammar Symbols
This paper describes a parsing model for speech with repairs that makes a clear separation between linguistically meaningful symbols in the grammar and operations specific to speech repair in the operation of the parser. This system builds a model of how unfinished constituents in speech repairs are likely to finish, and finishes them probabilistically with placeholder structure. These modified...
متن کاملImproved Syntactic Models for Parsing Speech with Repairs
This paper introduces three new syntactic models for representing speech with repairs. These models are developed to test the intuition that the erroneous parts of speech repairs (reparanda) are not generated or recognized as such while occurring, but only after they have been corrected. Thus, they are designed to minimize the differences in grammar rule applications between fluent and disfluen...
متن کاملA Unified Syntactic Model for Parsing Fluent and Disfluent Speech
This paper describes a syntactic representation for modeling speech repairs. This representation makes use of a right corner transform of syntax trees to produce a tree representation in which speech repairs require very few special syntax rules, making better use of training data. PCFGs trained on syntax trees using this model achieve high accuracy on the standard Switchboard parsing task.
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