Contains reports on one research project.Madeline Moses Fun

Miller, Perry L.

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XIII. COGNITIVE INFORMATION PROCESSINGProf. Murray EdenProf. Jonathan AllenProf. Barry A. BlesserProf. Francis F. LeeProf. Samuel J. MasonProf. William F. SchreiberProf. Donald E. TroxelProf. Ian T. YoungBrian E. BoyleBecky J. ClarkMichael A. CohenCharles H. CoxCaleb J. DrakeJames R. Ellis, Jr.Irvin S. EnglanderAli M. FakhrSteven G. FinnHarry S. ForsdickAlan M. GilkesRichard S. GoldhorDavid B. HarrisAcademic and Research StaffDr. Jack E. BowieDr. Ralph W. GershbergDr. Uri F. GronemannDr. Martin J. Hewlett*Dr. Conrade C. Jaffe'fDr. Perry L. MillerDr. Mary J. Naus$Dr. David M. OzonoffGraduate StudentsMarvin E. JerniganClayton K. S. KauRichard C. Kinnaird, Jr.John W. KlovstadTheodore T. KuklinskiSze-Ping KuoDonald S. LevinstoneCharles W. LynnLee C. MakowskiDennis V. MarsicanoDouglas O'ShaughnessyJohn E. OstrowskiDouglas B. PaulRoger S. PutnamDr. Stephanie E. SherFrancis X. CarrollJeffrey A. HagerichM. Sharon HunnicuttEric R. JensenKenneth A. OkinRobert A. PiankianKenneth P. WacksRobert J. ShillmanRobert D. SolomonJan A. SprietChristopher E. StrangioHoo-min D. ToongMargaret A. TurekBik-Kwoon TyeJoseph E. Wall, Jr.Allen W. WiegnerHarvey M. WolfsonKenway J. WongTonny Y. WongGregory W. ZackA. TEXT-TO-SPEECH CONVERSION1. LOCALLY ORGANIZED PARSER FOR SPOKEN INPUTMadeline Moses FundPerry L. MillerA Locally Organized PARSer for Spoken Input, (LPARS) has been implemented onthe TX-2 computer at the M. I. T. Lincoln Laboratory. It is designed to process contin-uous speech with the help of syntactic and semantic information.The LPARS system differs from traditional parsing methods in that it has no inher-ent left-to-right, or right-to-left, bias to its operation. Rather, it allows syntacticstructures to be recognized locally in any part of an utterance. In fact, several parsestructures may be built up simultaneously in different parts of the sentence, and laterconnected by searching for words that might reasonably exist between them.Thus words and phrases reliably recognized in any part of a sentence can be used*Research Affiliate from the Department of Biology.tPicker Foundation Fellow.*Also Assistant Professor of Psychology at Wellesley College.QPR No. 113 163(XIII. COGNITIVE INFORMATION PROCESSING)to help guide the search for further words to complete the sentence.Task DomainThe vocabulary of LPARS contains approximately 70 words. It recognizes a veryrestricted, but linguistically natural and interesting, subset of English. Its semanticsare defined in terms of a particular scene - a small two-room house containing people,furniture, fixtures, etc. - about which one may make statements, ask questions, tell avery simple story, or command the system to manipulate the scene. Sample input sen-tences are:"The coffeetable on which the ashtray is placed by Robert supports the dictionary.""What does the sidetable support?"OperationLPARS expects as input a string of phoneme candidates from a front-end phonemerecognizer. For the work reported here the input was prepared by a phonetic scramblerprogram that simulated front-end behavior, rather than by a real phoneme recognizer.The input phoneme candidates may be ambiguous (i. e., several possibilities may begiven for one segment). The input is also expected to contain a fairly large amount oferror.LPARS operates by first making an initial scan through a sentence looking for longerwords that are not too garbled. The scan returns a list of word candidates that matchwithin a given error tolerance to specified sections of the input. It is expected that someof these word candidates are incorrect, and indeed some may overlap.These word candidates are turned over to the higher level part of the system whichinitiates scans for small words and for more highly garbled words in the areas adjacentto and between the words found in the initial scan, and groups the words together intoparse structures. This processing is done systematically in an attempt to uncover theentire utterance.Simplified ExampleFigure XIII-1 is a simplified example of LPARS in operation. The input utteranceis the sentence: "The large coffeetable supports the green dictionary." In real oper-ation, the sentence would be spoken, analyzed by a front-end phoneme recognizer, andthen a string of phoneme candidates would be produced for input to LPARS. At present,this input is produced by a front-end simulator.In an initial scan through the sentence, LPARS tests the words in its vocabularyagainst the entire length of the input. This scan is made at a low phonetic distancelooking for longer words that are not too garbled. (Phonetic distance is a measure ofQPR No. 113 164(XIII. COGNITIVE INFORMATION PROCESSING)INPUT SENTENCE:'THE LARGE COFFEETABLE SUPPORTS THE GREEN DICTIONARY'(1) INITIAL SCAN: COFFEETABLE, GREEN(2) LOCAL HIGHER DISTANCE SCANS:COFFEETABLE(adi) (prep)(det) (verb)LARGE NIL(adi)(det)THEGREEN(adi) (noun)(det) DICTIONARYTHE(verb)(prep)NIL(3) PARTIAL PARSE TREE CONSTRUCTION:SSTRCH SENTNPAthe large coffeetable5ENDCHNPAthe green dictionary(4) CONNECTION OF PARTIAL PARSE TREES:S...------------- --- S- - - - - - - - - - - SSTRCH SENT N/ /N ENDCHNPNP AA A(5) RECOGNIZED SENTENCE:SSTRCH ENDCHSENTNP NPVERBtssupportsFig. XIII-1. Simple example of LPARS in operation.how closely the phonetic spelling of a word matches a section of input. ) Let us assume,in this instance, that the two word candidates "coffeetable" and "green" are found.These word candidates are turned over to the high-level part of the system, togetherwith a start-of-sentence character (STRCH) and an end-of-sentence character (ENDCH)which is added by the system.The higher level analysis (Fig. XIII-1) includes three fairly distinct stages. First,QPR No. 113STRCH ENDCH165(XIII. COGNITIVE INFORMATION PROCESSING)scans at higher phonetic distance are made based on fairly local cues, in the areas adja-cent to the word candidates. In this example, these local scans are very simple. Ascan in front of the noun uncovers the adjective "large" and a further scan in front ofthat word uncovers the determiner "the." A scan to the right of the noun for prepositionsand verbs fails to find any word candidates. This means that the verb "supports" is toogarbled to be picked up by either the initial scan or the higher phonetic distance selec-tive scan. Similar scans around the adjective "green" uncover the words "dictionary"and "the," but again fail to find the verb.After these higher distance scans have taken place, the system builds up as manyparse trees as it can in the sentence. If all words in the sentence have been found, thenthe entire sentence is constructed. Otherwise, the result is a number of partial parsetrees (PPTs) in different parts of the sentence.The system attempts to construct as many such PPTs as it can with the words it hasfound. In this example, it constructs only two PPTs. The first tree, " STRCH the largecoffeetable," is straightforward; the second, "the green dictionary ENDCH," is some-what unusual, since it contains an "ancestor link" (the link labeled "*"). The ancestorlink allows LPARS to recognize that, because of right recursion, many syntactic rela-tionships are possible, but to defer commitment until later, when an attempt is made tojoin this structure to another structure by proposing words between them.The third and final step in the parsing process is to connect PPTs to one another byusing the grammar to propose words that might exist between them. Any words pro-posed are tested against the input at even higher phonetic distances than the previousscans.In the present example, the algorithm discovers that the two parse trees can be con-nected if a verb is found between them. It therefore initiates a higher distance scanwhich succeeds in finding the verb " supports." Thus the entire sentence is recognized.Notice that this example is a simplified one. No erroneous words were found. Ina more realistic example, erroneous words would be found, some local structures con-taining these words would be built up, and, additionally, some erroneous local parsingsof the correct words could be constructed. Attempts to build out upon the erroneousstructures usually prove unsuccessful, while usually attempts to build out upon thecorrect structures eventually succeed.Experimental EvaluationThe LPARS system was evaluated by processing 50 sentences. These input sentenceswere produced by a front-end simulator which approximated the accuracy of the Vicens-Reddy phoneme recognizer. The simulator operates roughly as follows: 15% of the pho-nemes are deleted; 1 0% of the remaining phonemes are badly scrambled (i. e., a stopmight be changed into a vowel or fricative); for the remaining phonemes substitutionsQPR No. 113 166(XIII. COGNITIVE INFORMATION PROCESSING)are made at random from a restricted class of phonemes (i. e., a stop might be changedinto some other stop chosen at random).During the evaluation, the recognition of the sentence was considered successful ifthe correct sentence was included among several possible sentences that were found.When several sentences were found, the correct sentence was almost always the bestmatch.During evaluation of LPARS, the 50 sentences were processed with the followingresults: Twenty-three sentences were correctly recognized from words found only bythe initial local processing; nineteen sentences were correctly recognized by the PPTconnection algorithm, after the initial local processing; and three sentences were cor-rectly recognized by fallback methods. Two sentences resulted in incorrect recognition.In both cases, the sentence found differed from the input sentence in a single contentword. Finally, three sentences resulted in failure to find any possible sentence at all.Thus the overall success rate of LPARS with the 50 input sentences was 90%. We con-clude that, given a simulation of a fairly crude front end, the ideas embodied in LPARScan be made to work acceptably.This system was developed by the author and the research submitted to the Depart-ment of Electrical Engineering, M. I. T., in March 1973, in partial fulfillment of therequirements for the degree of Doctor of Philosophy. It has been published as LincolnLaboratory Report 503.QPR No. 113 167

Cognitive Information Processing

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Cognitive Information Processing

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