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

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

Generating Training Data for Medical Dictations

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 are generated in the normal course of a medical transcription operation. This paper presents a method of automatically generating texts that can take the place of literal transcriptions for training acoustic and language models. ATRS is an automatic transcription reconstruction system that can produce near-literal transcriptions with almost no human labor. We will show that (i) adapted acoustic models trained on ATRS data perform as well as or better than adapted acoustic models trained on literal transcriptions (as measured by recognition accuracy) and (ii) language models trained on ATRS data have lower perplexity than language models trained on non-literal data

Automatic Detection of Verbal Deception

https://digitalcommons.montclair.edu/all_books/1097/thumbnail.jp

A Parser For Real-Time Speech Synthesis Of Conversational Texts

In this paper, we concern ourselves with an application of text-to-speech for speech-impaired, deaf, and hard of hearing people. The application is unusual because it requires real-time synthesis of unedited, spontaneously generated conversational texts transmitted via a Telecommunications Device for the Deaf (TDD). We describe a parser that we have implemented as a front end for a version of the Bell Laboratories text-to-speech synthesizer (Olive and Liberman 1985). The parser prepares TDD texts for synthesis by (a) performing lexical regularization of abbreviations and some non-standard forms, and (b) identifying prosodic phrase boundaries. Rules for identifying phrase boundaries are derived from the prosodic phrase grammar described in-Bachenko and Fitzpatrick (1990). Following the parent analysis, these rules use a mix of syntactic and phonological factors to identify phrase boundaries but, unlike the parent system, they forgo building any hierarchical structure in order to bypass the need for a stacking mechamsm; this permits the system to operate in near real time. As a component of the text-to-speech system, the parser has undergone rigorous testing during a successful three-month field trial at an AT&T telecommunications center in California. In addition, laboratory evaluations indicate that the parser's performance compares favorably with human judgments about phrasing