word boundary- hypothesisation in hindi speech - Speech and ...

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aapteT I INTRODUCTION TO WORD BOVNDARY HYPOTHESISATION 1.1 Word boundary hypothesisation problem The problcrn of wzlrd boundary hyp~thesisation(Wl3H) arises in the contcxi "i' human communication with machines. The problem can be stated as follows: Given a string of symbols representing a sentence, word boundaries are to be placed in the symbol string to convert it into a string of words. This problem is relevant especially in the context of speech input to a machine, where the speaker does not consciously indicate the word boundaries while speaking. The work reported in this thesis is on the identification of clues to perform word boundary hypothesisation in a speech-to-text conversion system for the Indian language Hindi. The role of the word boundary hypothesisation problem in speech recognition can be understood if one examines the way humans speak. Normal speech is a sequence of sounds with very few pauses to indicate word boundaries. To convert speech into the corresponding text, one needs to identify the positions of the missing word boundaries. In speech recognition systems, the word boundaries may be obtained by matching a symbolic representation of the speech, produced by a speech signal-to- symbol converter, against a lexicon. However, this process, called lexical analysis, produces a large number of alternate word strings, when the input symbol sequence contains errors. Moreover, it also involves a large number of computations. Since a significant percentage of the speech recognition time is spent on lexical ~nalysis [Wolf and Woods 19801, one needs to simplify the lexical analysis to speed up the speech recognition. If some word boundaries can be identified before performing lexical analysis, the performance of the lexical analyser, both in number of computations and in accuracy, can be significantly improved. Consider the operation of a lexical analyser when some word boundaries are
\ known. The lexical analyser can now match each substring between word boundaries against the lexicon and produce word alternatives. Note that even if all the word boundaries are kncbwn, the lexical analyser still has to match the substrings between the boundaries against the lexicon, due to possibility of errors in the input symbols. From the word alternatives produced by the lexical match, sentence alternatives can be formed by constructing strings of the word alternatives. Since the word start and end points are known, many sentence alternatives that contain words spanning across word boundaries, which would have been produced in the absence of word boundaries, are eliminated. This results in a reduction in the number of alternatives for a sentence. Also, the time needed for lexical matching will be reduced. It can be reduced further, if the lexical matches for words can be done in parallel. Word boundary hypothesisation also simplifies the handling of unknown words, i.e., words which are not listed in the lexicon, such as names of persons and places. In the absence of word boundaries, on detecting an unknown word, the lexical analyser will have to search several alternative positions to detect the start of the next word. If word boundaries are known, it can start the lexical match from the next word boundary and leave the interpretation of the unknown word to later stages or to the user. The development of a word boundary hypothesiser also simplifies the design of a speech-to-text conversion system. The main objective of such a system is to generate a text corresponding to the input speech. Typically, a speech-to-text conversion system consists of a speech signal-to-symbol conversion system and a symbol-to-text conversion system. Assuming that the symbols correspond to the orthographic characters of the language, the symbol string produced by the signal-to-symbol conversion differs from the desired text mainly in the missing word boundaries. A symbol string without word boundaries is difficult to read even for humans. The main
Page 1 and 2: Dr. €3. YEGNANARAYANA Professor D
Page 3 and 4: ACKNOWLEDGEMENTS I thank my researc
Page 5 and 6: -3 SIGNIFICANCE OF WORD BOUNDARIES
Page 7 and 8: 6.6 Location of word boundaries fro
Page 9: ABSTRACT This thesis addresses the
Page 13 and 14: alounddhelongtableheagsnoddedenunis
Page 15 and 16: word boundaries into a text with wo
Page 17 and 18: words. The idea is to spot symbol s
Page 19 and 20: F1 position at a vowel-consonant bo
Page 21 and 22: -a2 A REVIEW OF THE STUDIES ON, WOR
Page 23 and 24: 1. When a midclass representation w
Page 25 and 26: particular interest is the performa
Page 27 and 28: was found that at broadclass level,
Page 29 and 30: in speaking rate indicate phrase bo
Page 31 and 32: y an estimator function E from the
Page 33 and 34: studies, the following issues are i
Page 35 and 36: 2. The effect of word boundary info
Page 37: * This selection can be done in man
Page 40 and 41: me : me:ra: metra: na: me:ra: na:m
Page 42 and 43: No. of alternatives 0-10 10-100 100
Page 44 and 45: 3.3.2 Results of lexical analysk wi
Page 46 and 47: had to be restricted to the above r
Page 48 and 49: Match Sentence nunber cost 1 2 3 4
Page 50 and 51: Match Sentence nuher cost 1 2 3 4 5
Page 52 and 53: 6 10 Number of 10 a1 ternate word s
Page 54 and 55: lexlcal analysis Mismatch cost Fig.
Page 56 and 57: had all word boundaries, is plotted
Page 58 and 59: Watch Sentence number cost 1 2 3 4
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Time lor lexical analysis 0 1 2 3 4
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Match Sentence tunter cost 1 2 3 4
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Time lor lexical analysis 1 0 1 2 3
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speech recognition process. On the
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proposed clues are based on the obs
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Case Markers: Pronouns : ka:, ki:,
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form. The above Hindi text was corr
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that if there are some substitution
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These measures were defined as foll
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Case Conjunc- Pronouns Verb Aux . A
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- I-' X Error in input text X Error
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% Error Fig.4.4 Results of word bou
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% Error Fig.4.5 A comparison of the
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0 I 1 I 0 10 20 30 40 50 60 % Error
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60 4 0 No. of subsentences No. of s
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LC1: A Hindi word can end either in
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Fig.4.10 along with the results for
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oundaries spotted) against system s
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from other words, such as 'national
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dictionary contained nearly 30000 w
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Vowel sequences Consonant sequences
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VC' were considered. It can be obse
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percentage of incorrect hypotheses
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% Error in input text 0 10 20 30 40
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% Error Fig.5.1 Results of word bou
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hypotheses produced by the lexical
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transformed into other nonword-inte
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WB hyp. errors 300 - 200 - ( v+> 10
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number of incorrect hypotheses will
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I--- Correctness 60 8o lmprovement
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% Error in input text 10 20 30 40 5
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will be smaller and hence S and P(y
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word boundary is within the consona
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clues except for clues of types CV'
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Pauses in speech, though small in n
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Several studies on English speech s
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Speaker VB hypotheses at various th
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Speaker UF hypotheses at various th
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Speaker UF hypotheses at various th
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Fig.6.1 The plot of pitch frequency
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From the results, it can be seen th
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speakers, with the incorrect hypoth
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durational effects. One possible ex
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From these results, it can be seen
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shown that the prosodic features of
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chapter 7 WORD BOUNDARY CLUES BASED
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consonants. Thus one can consider o
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Speaker VB hypotheses(VB:&) at vari
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Speaker UF hypotheses(UF:UI) at var
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Speaker UB hypotheses(UB:B) at vari
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high speaking rates. This is also b
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vowels preceding word boundaries, i
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word-final vowels than word-interna
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8.1 Introduction PERFORMANCE OF WOR
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Match Sentence nunber, cost 1 2 3 4
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75 Reduction in the time for lexica
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The reduction in the lexical analys
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Hatch Sentence nuher cost 1 2 3 4 5
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Chapter 9 SUMMARY AND CONCLUSIONS I
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In the last study (described in cha
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the language and lexical clues are
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REFERENCES Baken R.J. and Daniloff
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Cutler A. and Butterfield S. (199lb
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Prakash M., Ramana Rao G,V., Chandr
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Zelenski R. and Class F. (1983), A
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