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

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phonetic clues, in reducing the lexical analysis time is presented in chapter 8. The work is summarised in chapter 9 and some issues for further investigation aie also indicated in it.
-a2 A REVIEW OF THE STUDIES ON, WORD BOUNDARY HYPOTHESISATION 2.1 Introduction Several studies have been ic~~it~d in literature in which a number of word boundary hypothesisation techniques were described. These studies can be broadly divided into two categories:(i) studies which addressed the role of word boundaries in improving the performance of a lexical analyser, and (ii) studies which proposed some clues for word boundary hypothesisation. Most of the reported studies were for English, though a few other studies also addressed the problem of word boundary hypothesisation for other languages like German and Japanese. All of these studies were reviewed in this chapter. The review is also organised in two sections, with each section containing a review of the studies of the above two categories. Initially, in section 2.2, the studies which addressed the role of word boundaries in lexical analysis were reported. The studies on word boundary hypothesisation techniques were reported in the later section (section 2.3), which is further divided into four subsections, with each section reviewing the role of each of the four knowledge sources, language knowledge, lexical knowledge, prosodic knowledge, and acoustic-phonetic knowledge. In subsection 2.3.1, application of language knowledge for word boundary hypothesisation is reviewed. In subsection 2.3.2, word boundary hypothesisation techniques based primarily on the lexical knowledge are reviewed. In subsection 2.3.3, studies which examined the role of prosodic knowledge in word boundary hypothesisation are reviewed, and in subsection 2.3.4 word boundary hypothesisation techniques based on the acoustic-phonetic knowledge are described. This organisation of the studies is based on the primary clues used, although, in some studies, clues based on other knowledge sources were also used. For example, the Metrical Segmentation Strategy(MSS), described in subsection
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: F1 position at a vowel-consonant bo
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
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
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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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