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

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4.6 Sllmmary and Conclusions -5 WORD BOUNDARY CLUES BASED ON THE LEXICAL KNOWLEDGE 5.1 Introduction 5.2 Lexical clues for word boundary hypothesisation 5.3 Results of word boundary hypothesisation using lexical clues for correct input 5.3.1 Lexical clues in the form of vowel sequences 5.3.2 Lexical clues in the form of consonant sequences 5.3.3 Errors in word boundary hypotheses produced by lexical clues 5.4 Performance of the lexical clues for incorrect input 5.4.1 Estimation of the number of word boundaries detected and the number of errors 5.5 Effect of adding infrequent word-internal phoneme sequences to the lexical clues 5.6 Lexical clues from a small dictionary 5.7 Locating word boundaries from the hypotheses produced by the lexical clues 5.8 Summary and Conclusions -6 WORD BOUNDARY CLUES BASED ON THE PROSODIC KNOWLEDGE 6.1 Introduction 6.2 Word boundary hypothesisation using pause 6.3 Word-final vowel hypothesisation using duration 6.4 Word-final vowel hypothesisation using pitch 6.5 Word-final vowel hypothesisation using all prosodic clues
6.6 Location of word boundaries from word-final vowels 6.7 Summary and Conclusions -7 WORD BOUNDARY CLUES BASED ON THE ACOUSTIC-PHONETIC KNOWLEDGE 14 1 7.1 Introduction 141 7.2 Word boundary hypothesisation using first formant(F1) frequency 142 7.2.1 Algorithm for word boundary hypothesisation using changes in F1 position 142 7.2.2 Results of word boundary hypothesisation using changes in F1 position 7.3 Word boundary hypothesisation from changes in first formant energy 7.3.1 Algorithm for word final vowel detection using changes in F1 energy 7.3.2 Results of word boundary hypothesisation using changes in F1 energy 7.4 Summary and Conclusions Chapter 8 PERFORMANCE OF WORD BOUNDARY CLUES IN IMPROVING LEXICAL ANALYSIS 8.1 Introduction 8.2 Studies on the reduction in lexical analysis time due to word boundary hypothesisation 8.2.1 Performance of language clues in improving lexical analysis 8.2.2 Performance of lexical clues in improving lexical analysis 8.2.3 Performance of prosodic clues in improving lexical analysis 8.2.4 Performance of acoustic-phonetic clues in improving lexical analysis 8.3 Summary and Conclusions
Page 1 and 2: Dr. €3. YEGNANARAYANA Professor D
Page 3 and 4: ACKNOWLEDGEMENTS I thank my researc
Page 5: -3 SIGNIFICANCE OF WORD BOUNDARIES
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 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.
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had all word boundaries, is plotted
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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
Page 185 and 186:
Cutler A. and Butterfield S. (199lb
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Prakash M., Ramana Rao G,V., Chandr
Page 189 and 190:
Zelenski R. and Class F. (1983), A
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