Joan Bachenko

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We describe an experimental text-to-speech system that uses information about syntactic constituency, adjacency to a verb, and constituent length to determine prosodic phrasing for synthetic speech. A central goal of our work has been to characterize "discourse neutral" phrasing, i.e. sentence-level phrasing patterns that are independent of discourse(More)
Our goal is to use natural language processing to identify deceptive and non-deceptive passages in transcribed narratives. We begin by motivating an analysis of language-based deception that relies on specific linguistic indicators to discover deceptive statements. The indicator tags are assigned to a document using a mix of automated and manual methods.(More)
In automatic speech recognition (ASR) enabled applications for medical dictations, corpora of literal transcriptions of speech are critical for training both speaker independent and speaker adapted acoustic models. Obtaining these transcriptions is both costly and time consuming. Non-literal transcriptions, on the other hand, are easy to obtain because they(More)
While various aspects of syntactic structure have been shown to bear on the determination of phrase-level prosody, the text-to-speech field has lacked a robust working system to test the possible relations between syntax and prosody. We describe an implemented system which uses the deterministic parser Fidditch to create the input for a set of prosody(More)
In this paper we present an initial experiment in the estimation of the amenability of new domains to true/false classification. We choose four domains, two of which have been classified for deception, and use the out-of-rank distance measure on n-grams to aid in deciding whether the third and fourth domains are amenable to T/F classification. We then use a(More)