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

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aapte7 3 SIGNIFICANCE OF WORD BOUNDARIES IN LEXICAL ANALYSIS 3.1 Introduction In the first chapter, arguments v:ere presented to justify the need fi3: pcrfcrming word boundary hypothesisation before performing lexical analysis. Three reasons were given in this regard: (i) improvement in the lexical matching, (ii) easier handling of unknown words, and (iii) easy development of speech-to-text conversion systems. Reasons (ii) and (iii) are self evident. In this chapter, studies are reported establishing (i), i.e., improvement in lexical analysis due to word boundary hypothesisation. There are two ways in which the word boundary hypothesisation can affect lexical analysis: (i) by reducing the number of alternate word strings for a given input sentence, and (ii) by reducing the computation involved in lexical matching. While these two are related, their implications in the context of speech-to-text conversio~? are different. A reduction in the number of alternate word strings means a reduction in the computations for the later stages of processing, such as syntactic and semantic analyses. On the other hand, a reduction in the lexical analysis time means a reduction in the total time for speech recognition. Since lexical analysis constitutes a major part of the speech recognition task, a reduction in the lexical analysis time can significantly speed up the overall recognition. In chapter 2 (section 2.2), two earlier studies on the effect of word boundaries on lexical analysis were described. These studies established that the knowledge of word boundaries (even if it is only partial) can improve the performance of the lexical analyser, especially when the input is only partially known (midclass or broadclass strings) as likely in a speech recognition system. However these studies have not addressed the following issues in detail: 1. The effect of errors in input symbols on the performance of a lexical analyser.
2. The effect of word boundary information on the performance of a lexical analyser. Studies were conducted to addkess these issues. A lexical analyser was developed to study its performance f ~r two types cif inputs: (i) input sentences without word boundaries, and (ii) input sentences with word boundaries. The performance of the lexical analyser was estimated using two measures: (i) the number of alternate word strings for a given input sentence, and (ii) the time spent on the lexical analysis, for varying input errors. In the next section, the lexical analyser used in the studies is described. In section 3.3, the performance of the lexical analyser without word boundaries in input sentences is estimated for various input errors. In section 3.4, the performance of the lexical analyser is estimated for the input sentences with word boundaries. In section 3.5, the results of the above two studies are compared, and the effect of word boundaries on lexical analysis is discussed. In these studies it was assumed that all the word boundaries in the input are correct, which is not realistic. This constraint is removed in the next study, reported in section 3.6, in which the performance of the lexical analyser was estimated for varying number of word boundary errors. These results and their implications in speech recognition are discussed in section 3.7. 3.2 Lexical analyser The lexical analyser matches the input sequence of phonemes representing a sentence against a prestored lexicon to produce alternate word strings matching the input. Since the input phoneme sequence may contain errors, approximate string matching [Hall and Dowling 19801 is used to produce the word strings. Hence one may obtain several alternate word strings matching the same input phoneme string but at different matching costs. The cost associated with a word sequence indicates the amount of its mismatch with the input phoneme sequence. Even for the same cost, one
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 and 10: ABSTRACT This thesis addresses the
Page 11 and 12: \ known. The lexical analyser can n
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: studies, the following issues are i
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
Page 60 and 61: Time lor lexical analysis 0 1 2 3 4
Page 62 and 63: Match Sentence tunter cost 1 2 3 4
Page 64 and 65: Time lor lexical analysis 1 0 1 2 3
Page 66 and 67: speech recognition process. On the
Page 68 and 69: proposed clues are based on the obs
Page 70 and 71: Case Markers: Pronouns : ka:, ki:,
Page 72 and 73: form. The above Hindi text was corr
Page 74 and 75: that if there are some substitution
Page 76 and 77: These measures were defined as foll
Page 78 and 79: Case Conjunc- Pronouns Verb Aux . A
Page 80 and 81: - I-' X Error in input text X Error
Page 82 and 83: % 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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