Enhancing automatic speech recognition for mathematical applications via incremental parsing

Automatic speech recognition and automatic speech understanding systems have, over recent years, improved to the extent that they are used in many practical applications ranging from dictation systems to voice control of household devices (through systems such as Alexa and Amazon Echo) and dialogue systems for telephone shopping and customer services for utility companies. However, advancements in speech input systems for mathematical applications have tended to lag behind those for more general or commercial situations. Our system TalkMaths, which has been under development for several years, is an exception to this, and allows spoken dictation and editing of mathematical text (in standard mathematical notation) using relatively natural spoken language commands. However, up to now, correcting a mistake in a spoken form of a mathematical expression has required a complete re-parse of the spoken input, which is time consuming and potentially frustrating to the user. In this paper, we discuss ways of improving on this situation using incremental approaches to parsing. These were first devised to make the parsing and compilation of computer program code more efficient, by only re-parsing those parts of the program code which had actually changed, and merging the parse trees of the unchanged and modified parts of the code. We adapt this methodology to allow editing of spoken forms of mathematical expressions, and their associated parse trees, and describe and discuss initial experiments to compare the performance of these novel methods with those of more conventional approaches

Parse Forest Diagnostics with Dr. Ambiguity

In this paper we propose and evaluate a method for locating causes of ambiguity in context-free grammars by automatic analysis of parse forests. A parse forest is the set of parse trees of an ambiguous sentence. % an output of a static ambiguity detection tool that has detected ambiguity in a context-free grammar or of a general parser that has accidentally parsed an ambiguous sentence. Deducing causes of ambiguity from observing parse forests is hard for grammar engineers because of (a) the size of the parse forests, (b) the complex shape of parse forests, and (c) the diversity of causes of ambiguity. We first analyze the diversity of ambiguities in grammars for programming languages and the diversity of solutions to these ambiguities. Then we introduce \drambiguity: a parse forest diagnostics tools that explains the causes of ambiguity by analyzing differences between parse trees and proposes solutions. We demonstrate its effectiveness using a small experiment with a grammar for Java 5