A markup language for text-to-speech synthesis.

Text-to-speech synthesizers must process text, and therefore require some knowledge of text structure. While many TTS systems allow for user control by means of ad hoc ‘escape sequences’, there remains to date no adequate and generally agreed upon system-independent standard for marking up text for the purposes of synthesis. The present paper is a collaborative effort between two speech groups aimed at producing such a standard, in the form of an SGML-based markup language that we call STML — Spoken Text Markup Language. The primary purpose of this paper is not to present STML as a fait accompli, but rather to interest other TTS research groups to collaborate and contribute to the development of this standard

Analysis of clinical uncertainties by health professionals and patients: an example from mental health

<p>Abstract</p> <p>Background</p> <p>The first step in practising Evidence Based Medicine (EBM) has been described as translating clinical uncertainty into a structured and focused clinical question that can be used to search the literature to ascertain or refute that uncertainty. In this study we focus on questions about treatments for schizophrenia posed by mental health professionals and patients to gain a deeper understanding about types of questions asked naturally, and whether they can be reformulated into structured and focused clinical questions.</p> <p>Methods</p> <p>From a survey of uncertainties about the treatment of schizophrenia we describe, categorise and analyse the type of questions asked by mental health professionals and patients about treatment uncertainties for schizophrenia. We explore the value of mapping from an unstructured to a structured framework, test inter-rater reliability for this task, develop a linguistic taxonomy, and cross tabulate that taxonomy with elements of a well structured clinical question.</p> <p>Results</p> <p>Few of the 78 Patients and 161 clinicians spontaneously asked well structured queries about treatment uncertainties for schizophrenia. Uncertainties were most commonly about drug treatments (45.3% of clinicians and 41% of patients), psychological therapies (19.9% of clinicians and 9% of patients) or were unclassifiable.(11.8% of clinicians and 16.7% of patients). Few naturally asked questions could be classified using the well structured and focused clinical question format (i.e. PICO format). A simple linguistic taxonomy better described the types of questions people naturally ask.</p> <p>Conclusion</p> <p>People do not spontaneously ask well structured clinical questions. Other taxonomies may better capture the nature of questions. However, access to EBM resources is greatly facilitated by framing enquiries in the language of EBM, such as posing queries in PICO format. People do not naturally do this. It may be preferable to identify a way of searching the literature that more closely matches the way people naturally ask questions if access to information about treatments are to be made more broadly available.</p

Tailoring Lexical Choice To The User&apos;s Vocabulary In Multimedia Explanation Generation

In this paper, we discuss the different strategies used in COMET (COordinated Multimedia Explanation Testbed) for selecting words with which the user is familiar. When pictures cannot be used to disambiguate a word or phrase, COMET has four strategies for avoiding unknown words. We give examples for each of these strategies and show how they are implemented in COMET