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<Paper uid="C90-2011">
  <Title>An Augmented Chart Data Structure with Efficient Word Lattice Parsing Scheme In Speech Recognition Applications</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1. Introduction
</SectionTitle>
    <Paragraph position="0"> In this paper, the conventional chart data structure has been augmented for efficient word lattice parsing to handle the high degree of ambiguities encountered in speech recognition applications. A word lattice is a set of word hypotheses produced by some acoustic signal processor in continuous speech recognition applications which possibly includes problems such as word boundary overlapping, lexical ambiguities, missing or extra phones, recognition uncertainty and errors, etc. The purpose of parsing such a word lattice is to efficiently and accurately obtain the most promising candidate sentence at acceptable computation complexity by means of grammatical constraints and appropriate data structure design. For example, in the process of continuous speech recognition, it happened very often that not oaly more than one words may be produced for a given segment of speech (such as homonyms, especially for some languages with large number of homonyms such as Chinese language (Lee, 1987) ), but many competing word hypotheses can be produced at overlapping, adjoining, or separate sediments of the acoustic sig-nal without a set of aligned word boundaries. T,,,is will result in huge number of sentence hypotheses, each of which formed by one combination of a sequence of word hypotheses, such that exhaustively parsing all these sentence hypotheses with a conventionai text parser is computational inefficient or even prohibitively difficult. A really efficient approach is therefore desired. Several algorithms for parsing such word lattices had been proposed (Tomita, 1986; 60 1 Chow, 1989). These algorithms had been shown to be ve~:y efficient in parsing less ambiguous natural lartguages such as English obtained in speech recognition. However, all of them are primarily strictly from left-to-right, thus with relatively limited applications for cases in which other strategies such as island-driven (Hayes, 1986) or even right-to-left are more useful (Huang, 1988), for example, corrupted word lattice with extra, missing or erroneous phones in speech recognition (Ward, 1988). On the other hand, chart has been an efficient working structure widely used in many natural language processing systems and has been shown to be a very effective approach (Kay, 1980), but it is basically designed to parse a sequence of fixed and known words instead of ambiguous word lattice. In this paper, the conventional chart is therefore extended or augmented such that it is able to represent a word lattice; while the conventional functions, operations and properties of a chart parser as well as some useful extensions such as the use of lexicalized grammars and island-driven parsing will not be affected by the augmentation at all. Therefore t2he augmented chart parsing proposed in this paper is a very efficient and attractive parsing scheme for many language processing problems in speech recognition applications. A word lattice parser based on the augmented chart data structure proposed here has been implemented and tested for Chinese language and the preliminary results are very encouraging.</Paragraph>
    <Paragraph position="1"> In the following, Section 2 introduces the concept of the augmented chart and Section 3 describes the mapping procedure to map an input word lattice to the augmented chart. The parsing scheme and some fitrther extensions are discussed in Sections 4; while some preliminary experimental results are presented irt Section 5. Concluding remarks are finally given in Section 6.</Paragraph>
  </Section>
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