{ "paper_id": "C90-2011", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T12:36:37.784868Z" }, "title": "An Augmented Chart Data Structure with Efficient Word Lattice Parsing Scheme In Speech Recognition Applications", "authors": [ { "first": "Lee-Feng", "middle": [], "last": "Chien", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Taiwan University", "location": { "settlement": "Taipei", "country": "Taiwan, R.O.C" } }, "email": "" }, { "first": "K", "middle": [ "J" ], "last": "Chen", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Lin-Shan", "middle": [], "last": "Lee", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Taiwan University", "location": { "settlement": "Taipei", "country": "Taiwan, R.O.C" } }, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "In this paper, an augmented chart data structure with efficient word lattice parsing scheme in speech recognition applications is proposed. The augmented chart and the associated parsing, algorithm can represent and parse very efficiently a lattice of word hypotheses produced in speech recognition with high degree of lexical ambiguity .without changing the fundamental principles of chart parsing. Every word !attice can be mapped to the augmented chart with the ordering and connection relation among word hypotheses being well preserved in the augmented chart. A jump edge is defined to link edges representing word hypotheses physically separated but practically possible to be connected. Preliminary experimental results show that with the augmented chart parsing all possible constituents of the input word lattice can be constructed and no constituent needs to be built more than once. This will reduce the computation complexity significantly especially when serious lexical ambiguity exists in the input word lattice as in many speech recognition problems. This augmented chart parsing is thus a very useful and efficient approach to language processing problems in speech recognition applications.", "pdf_parse": { "paper_id": "C90-2011", "_pdf_hash": "", "abstract": [ { "text": "In this paper, an augmented chart data structure with efficient word lattice parsing scheme in speech recognition applications is proposed. The augmented chart and the associated parsing, algorithm can represent and parse very efficiently a lattice of word hypotheses produced in speech recognition with high degree of lexical ambiguity .without changing the fundamental principles of chart parsing. Every word !attice can be mapped to the augmented chart with the ordering and connection relation among word hypotheses being well preserved in the augmented chart. A jump edge is defined to link edges representing word hypotheses physically separated but practically possible to be connected. Preliminary experimental results show that with the augmented chart parsing all possible constituents of the input word lattice can be constructed and no constituent needs to be built more than once. This will reduce the computation complexity significantly especially when serious lexical ambiguity exists in the input word lattice as in many speech recognition problems. This augmented chart parsing is thus a very useful and efficient approach to language processing problems in speech recognition applications.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "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; 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.", "cite_spans": [ { "start": 985, "end": 996, "text": "(Lee, 1987)", "ref_id": "BIBREF6" }, { "start": 1571, "end": 1585, "text": "(Tomita, 1986;", "ref_id": "BIBREF9" }, { "start": 1586, "end": 1597, "text": "Chow, 1989)", "ref_id": "BIBREF0" }, { "start": 1911, "end": 1924, "text": "(Hayes, 1986)", "ref_id": "BIBREF3" }, { "start": 1963, "end": 1976, "text": "(Huang, 1988)", "ref_id": null }, { "start": 2077, "end": 2089, "text": "(Ward, 1988)", "ref_id": "BIBREF10" }, { "start": 2264, "end": 2275, "text": "(Kay, 1980)", "ref_id": "BIBREF5" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "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 ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "The conventional chart parsing algorithm was designed to parse a sequence of words. In this section the chart is augmented for parsing word lattices. The purpose is to efficiently and accurately find out all grammatically valid sentence hypotheses and their sentence structures from a given word lattice based on a grammar.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Augmented Chart", "sec_num": "2." }, { "text": "A word lattice W is a partially ordered set of word hypotheses, W = {w 1 ..... win}, where each word hypothesis w i, i=l .... ,m, is characterized by begin, the beginning point, end, the ending point, cat, the category, phone, the associated phonemes, and name, the word name of the word hypothesis. These word hypotheses are sorted in the order of their ending points; that is, for every pair of word hypotheses w i and wj, i, where V is a sequence of vertices and E is a set of edges. Eachvertex in V represents an end point of some word hypotheses in the input word lattice, while the edge set is divided into three disjoint groups: inactive, active and jump edges. As were used in a conventional chart, an inactive edge is a data structure to represent a completed constituent, while an active edge represents an incomplete constituent which needs some other complete constituents to compose a larger one. A jump edge, however, is a functional edge which links two different edges to indicate their connection relation (described below) and guide the parser to search through all edges connected to each active edge during parsing. The pailial ordering relation among the edges in the augmented chart can first be defined according to the order of the boundary vertices. Two edge E i and Ej are then said to be connected (i.e. EConn(E i, Ej) = true) only when the end vertex of one of them is the begin vertex of the other, or there exists a jump edge linking them together. For example, in the chart representation of the sample word lattice in Fig: 1 (on the bottom of the figure, the details will be explained in the next section), EConn(E 3, E 6) = true due to the existence of J~np3 linking E 3 and E 6, but EConn(E 1 , Th~ ~r~k: ~m:l lanio~ w I:(5, 20, N, t4d,Tad) w3:('/5, 42. V, t~) wS: (45.60, N;tis, thi~)" }, "FIGREF2": { "uris": null, "type_str": "figure", "num": null, "text": "The situation in which additional:word hypotheses are inserted After the above additional word hypotheses insertion, every boundary point (either beginning or ending) of any word hypothesis of W should then be mapped to a vertex in the chart. All these word boundary points (wbp's) have to be first sorted into an ordered sequence (indicated by a function Order(x), where x is any wbp); the definition of Order(x) is as follows. To any pair of wbp's x and y, if x and y are distinct then their order is based on order in time; if x and y are identical then the begi,ming wbp (denoted by b) L,; after the ending wbp (denoted by e). For each wbp x, the corresponding vertex is then assigned depending on its preceding wbp y as described below.As was shown inFig. 3, for totally four possible cases of x and y, i.e. bb (y is a beginning wbp and x is also\" a beginning wbp), be, eb, ee, only for the case be (y is a beginning wbp but x an ending wbp), two different vertices should be assigned to x and y to preserve the ord.::ring relation between the corresponding word hypotheses of x and y. But in all the other three cases,x and y can l:u'. given the same vertex. Let the function Vertex(x) denotes this assignment. the word boundary points Now, for each word hypothesis w i , an initial inactive edge can be constructed. The function Edge(w i) for a word hypothesis w i is then exactly specified by the two vertices assigned to the two wbp's of w i , i.e. Edge(w i) = < Vertex(begin(wi)), Vertex(end(wi))>. Finally, for any pair of vertices v i and vj, if there isn't any complete initial inactive edge existing between them, a jump edge from v i to vj is constructed to link v i and vj. Using the above procedure,Fig. 1also shows the mapping results of the sample word lattice. The sorted wbp's (specified by a time scale and whether it is a beginning or ending wbp) are on the middle of the figure, and the resulting initial chart is on the bottom. It can be shown that the above mapping procedure has the following nice properties: first, the ordering and connection relations among all word hypotheses in the word lattice can be completely preserved among the corresponding edges in the augmented chart; second, when the input word lattice can be reduced to a simple sequence of word hypotheses, the augmented chart representation can also be reduced to a conventional chart representation." }, "FIGREF3": { "uris": null, "type_str": "figure", "num": null, "text": "augmented chart parsing works, a bottom-up and left-to-right parser based on the proposed augmented chart (also capable of perforating conventional chart parsing) has been implemented and tested in some preliminary experiments. The test data base includes a large number of Chinese word lattices obtained from an acoustic signal processor which recognizes Mandarin speech. Due to the existence of large number of homonyms in Chinese language and uncertainty and errors in speech recognition, very high degree of Iexical ambiguity exists in the input lattices. One example of such Chinese word lattice is inFig. 4. The results show that, all possibte constituents for the input word lattice can be constructed and no any constituent needs to be built more than once using the augmented chart parsing. According to the experimental results, the edge reduction ratio (the ratio of the total number of edges built in the augmented chart parsing to the total number of edges built in conventional chart parsing) is on the order of 1/30 ~ 1/80 for our input Chinese word lattices." } } } }