A conversational interface to news retrieval

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (p. 51-53).by James C. Clemens.M.Eng

Using automatic speech recognition to evaluate Arabic to English transliteration

Increased travel and international communication has led to an increased need for transliteration of Arabic proper names for people, places, technical terms and organisations. There are a variety of available Arabic to English transliteration systems such as Unicode, the Buckwalter Arabic transliteration, and ArabTeX. The transliteration tables have been developed and used by researchers for many years, but there are only limited attempts to evaluate and compare different transliteration systems. This thesis investigates whether or not speech recognition technology could be used to evaluate different Arabic-English transliteration systems. In order to do so there were 5 main objectives: firstly, to investigate the possibility of using English speech recognition engines to recognize Arabic words; secondly, to establish the possibility of automatic transliteration of diacritised Arabic words for the purpose of creating a vocabulary for the speech recognition engine; thirdly, to explore the possibility of automatically generating transliterations of non diacritised Arabic words; fourthly to construct a general method to compare and evaluate different transliteration; and finally, to test the system and use it to experiment with new transliterations ideas

Toward effective conversational messaging

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1995.Includes bibliographical references (leaves 118-123).Matthew Talin Marx.M.S

SPEECH TO CHART: SPEECH RECOGNITION AND NATURAL LANGUAGE PROCESSING FOR DENTAL CHARTING

Typically, when using practice management systems (PMS), dentists perform data entry by utilizing an assistant as a transcriptionist. This prevents dentists from interacting directly with the PMSs. Speech recognition interfaces can provide the solution to this problem. Existing speech interfaces of PMSs are cumbersome and poorly designed. In dentistry, there is a desire and need for a usable natural language interface for clinical data entry. Objectives. (1) evaluate the efficiency, effectiveness, and user satisfaction of the speech interfaces of four dental PMSs, (2) develop and evaluate a speech-to-chart prototype for charting naturally spoken dental exams. Methods. We evaluated the speech interfaces of four leading PMSs. We manually reviewed the capabilities of each system and then had 18 dental students chart 18 findings via speech in each of the systems. We measured time, errors, and user satisfaction. Next, we developed and evaluated a speech-to-chart prototype which contained the following components: speech recognizer; post-processor for error correction; NLP application (ONYX) and; graphical chart generator. We evaluated the accuracy of the speech recognizer and the post-processor. We then performed a summative evaluation on the entire system. Our prototype charted 12 hard tissue exams. We compared the charted exams to reference standard exams charted by two dentists. Results. Of the four systems, only two allowed both hard tissue and periodontal charting via speech. All interfaces required using specific commands directly comparable to using a mouse. The average time to chart the nine hard tissue findings was 2:48 and the nine periodontal findings was 2:06. There was an average of 7.5 errors per exam. We created a speech-to-chart prototype that supports natural dictation with no structured commands. On manually transcribed exams, the system performed with an average 80% accuracy. The average time to chart a single hard tissue finding with the prototype was 7.3 seconds. An improved discourse processor will greatly enhance the prototype's accuracy. Conclusions. The speech interfaces of existing PMSs are cumbersome, require using specific speech commands, and make several errors per exam. We successfully created a speech-to-chart prototype that charts hard tissue findings from naturally spoken dental exams

Automatic translation of formal data specifications to voice data-input applications.

This thesis introduces a complete solution for automatic translation of formal data specifications to voice data-input applications. The objective of the research is to automatically generate applications for inputting data through speech from specifications of the structure of the data. The formal data specifications are XML DTDs. A new formalization called Grammar-DTD (G-DTD) is introduced as an extended DTD that contains grammars to describe valid values of the DTD elements and attributes. G-DTDs facilitate the automatic generation of Voice XML applications that correspond to the original DTD structure. The development of the automatic application-generator included identifying constraints on the G-DTD to ensure a feasible translation, using predicate calculus to build a knowledge base of inference rules that describes the mapping procedure, and writing an algorithm for the automatic translation based on the inference rules.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .H355. Source: Masters Abstracts International, Volume: 45-01, page: 0354. Thesis (M.Sc.)--University of Windsor (Canada), 2006

Word Importance Modeling to Enhance Captions Generated by Automatic Speech Recognition for Deaf and Hard of Hearing Users

People who are deaf or hard-of-hearing (DHH) benefit from sign-language interpreting or live-captioning (with a human transcriptionist), to access spoken information. However, such services are not legally required, affordable, nor available in many settings, e.g., impromptu small-group meetings in the workplace or online video content that has not been professionally captioned. As Automatic Speech Recognition (ASR) systems improve in accuracy and speed, it is natural to investigate the use of these systems to assist DHH users in a variety of tasks. But, ASR systems are still not perfect, especially in realistic conversational settings, leading to the issue of trust and acceptance of these systems from the DHH community. To overcome these challenges, our work focuses on: (1) building metrics for accurately evaluating the quality of automatic captioning systems, and (2) designing interventions for improving the usability of captions for DHH users. The first part of this dissertation describes our research on methods for identifying words that are important for understanding the meaning of a conversational turn within transcripts of spoken dialogue. Such knowledge about the relative importance of words in spoken messages can be used in evaluating ASR systems (in part 2 of this dissertation) or creating new applications for DHH users of captioned video (in part 3 of this dissertation). We found that models which consider both the acoustic properties of spoken words as well as text-based features (e.g., pre-trained word embeddings) are more effective at predicting the semantic importance of a word than models that utilize only one of these types of features. The second part of this dissertation describes studies to understand DHH users\u27 perception of the quality of ASR-generated captions; the goal of this work was to validate the design of automatic metrics for evaluating captions in real-time applications for these users. Such a metric could facilitate comparison of various ASR systems, for determining the suitability of specific ASR systems for supporting communication for DHH users. We designed experimental studies to elicit feedback on the quality of captions from DHH users, and we developed and evaluated automatic metrics for predicting the usability of automatically generated captions for these users. We found that metrics that consider the importance of each word in a text are more effective at predicting the usability of imperfect text captions than the traditional Word Error Rate (WER) metric. The final part of this dissertation describes research on importance-based highlighting of words in captions, as a way to enhance the usability of captions for DHH users. Similar to highlighting in static texts (e.g., textbooks or electronic documents), highlighting in captions involves changing the appearance of some texts in caption to enable readers to attend to the most important bits of information quickly. Despite the known benefits of highlighting in static texts, research on the usefulness of highlighting in captions for DHH users is largely unexplored. For this reason, we conducted experimental studies with DHH participants to understand the benefits of importance-based highlighting in captions, and their preference on different design configurations for highlighting in captions. We found that DHH users subjectively preferred highlighting in captions, and they reported higher readability and understandability scores and lower task-load scores when viewing videos with captions containing highlighting compared to the videos without highlighting. Further, in partial contrast to recommendations in prior research on highlighting in static texts (which had not been based on experimental studies with DHH users), we found that DHH participants preferred boldface, word-level, non-repeating highlighting in captions

The Design and Application of an Acoustic Front-End for Use in Speech Interfaces

This thesis describes the design, implementation, and application of an acoustic front-end. Such front-ends constitute the core of automatic speech recognition systems. The front-end whose development is reported here has been designed for speaker-independent large vocabulary recognition. The emphasis of this thesis is more one of design than of application. This work exploits the current state-of-the-art in speech recognition research, for example, the use of Hidden Markov Models. It describes the steps taken to build a speaker-independent large vocabulary system from signal processing, through pattern matching, to language modelling. An acoustic front-end can be considered as a multi-stage process, each of which requires the specification of many parameters. Some parameters have fundamental consequences for the ultimate application of the front-end. Therefore, a major part of this thesis is concerned with their analysis and specification. Experiments were carried out to determine the characteristics of individual parameters, the results of which were then used to motivate particular parameter settings. The thesis concludes with some applications that point out, not only the power of the resulting acoustic front-end, but also its limitations