Parsing for prosody: What a text-to-speech system needs from syntax

The authors describe an experimental text-to-speech system that uses a syntactic parser and prosody rules to determine prosodic phrasing for synthesized speech. It is shown that many aspects of sentence analysis that are required for other parsing applications, e.g., machine translation and question answering, become unnecessary in parsing for text-to-speech. It is possible to generate natural-sounding prosodic phrasing by relying on information about syntactic category type, partial constituency, and length; information about clausal and verb phrase constituency, predicate-argument relations, and prepositional phrase attachment can be bypassed

Truth and Deception at the Rhetorical Structure Level

This paper furthers the development of methods to dis- tinguish truth from deception in textual data. We use rhetorical structure theory (RST) as the analytic framework to identify systematic differences between deceptive and truthful stories in terms of their coher- ence and structure. A sample of 36 elicited personal stories, self-ranked as truthful or deceptive, is manu- ally analyzed by assigning RST discourse relations among each story’s constituent parts. A vector space model (VSM) assesses each story’s position in multi- dimensional RST space with respect to its distance from truthful and deceptive centers as measures of the story’s level of deception and truthfulness. Ten human judges evaluate independently whether each story is deceptive and assign their confidence levels (360 evaluations total), producing measures of the expected human ability to recognize deception. As a robustness check, a test sample of 18 truthful stories (with 180 additional evaluations) is used to determine the reli- ability of our RST-VSM method in determining decep- tion. The contribution is in demonstration of the discourse structure analysis as a significant method for automated deception detection and an effective complement to lexicosemantic analysis. The potential is in developing novel discourse-based tools to alert information users to potential deception in computer- mediated texts

A Rule-Based Phrase Parser for Real-Time Text-To-Speech Synthesis

Text-to-speech systems are currently designed to work on complete sentences and paragraphs, thereby allowing front end processors access to large amounts of linguistic context. Problems with this design arise when applications require text to be synthesized in near real time, as it is being typed. How does the system decide which incoming words should be collected and synthesized as a group when prior and subsequent word groups are unknown? We describe a rule-based parser that uses a three cell buffer and phrasing rules to identify break points for incoming text. Words up to the break point are synthesized as new text is moved into the buffer; no hierarchical structure is built beyond the lexical level. The parser was developed for use in a system that synthesizes written telecommunications by Deaf and hard of hearing people. These are texts written entirely in upper case, with little or no punctuation, and using a nonstandard variety of English (e.g. WHEN DO I WILL CALL BACK YOU). The parser performed well in a three month field trial utilizing tens of thousands of texts. Laboratory tests indicate that the parser exhibited a low error rate when compared with a human reader

Verification and implementation of language-based deception indicators in civil and criminal narratives

Our goal is to use natural language processing to identify deceptive and nondeceptive 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. Once the tags are assigned, an interpreter automatically discriminates between deceptive and truthful statements based on tag densities. The texts used in our study come entirely from “real world ” sources—criminal statements, police interrogations and legal testimony. The corpus was hand-tagged for the truth value of all propositions that could be externally verified as true or false. Classification and Regression Tree techniques suggest that the approach is feasible, with the model able to identify 74.9 % of the T/F propositions correctly. Implementation of an automatic tagger with a large subset of tags performed well on test data, producing an average score of 68.6 % recall and 85.3 % preci