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

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had to be restricted to the above range as the lexical analyser took several hours of computation, even for this small error. The results show that the number of alternate word strings for an input sentence increase rapidly with increase in the mismatch. The number of alternate word strings was plotted against the mismatch cost for one sentence in Fig.3.3. It can be seen that the growth is nearly exponential: for example, an increase of one in the mismatch cost, the number of alternate word strings increased by a factor of 6. The performance of the lexical analyser was also studied in terms of the time spent on the lexical analysis. These results are shown in Table - 3.4. It can be seen that the time spent on lexical analysis also increases rapidly with increase in mismatch. A plot of the time against the mismatch cost for one sentence is shown in Fig.3.4. It can be seen that the time spent on lexical analysis also increases exponentially with increasing mismatch. The above results show clearly that both the number of alternate word strings matching a sentence and the time spent on matching increase rapidly with increasing mismatch cost used for matching. 3.4 Lexical analysis with word boundaries To estimate the improvement in the performance of the lexical analyser when the word boundaries are known in the input, the lexical matching algorithm was again applied on the sentences with all the word boundaries present. Since word boundaries are known, the lexical match was performed for each word separately to obtain alternate word strings for each word. The alternatives for the sentence were obtained by combining these word strings. The results of the lexical match in terms of the number of alternate word strings for the sentences are shown.in Table - 3.5. The results show that the number of alternate word strings for a sentence still
Number of a1 ternate word sequences Mismatch cost Fig. 3.3 Results of lexical analysis on a sentence without word boundaries. The figure shows the number of alternate word sequences matching the sentence at various mismatch costs. It can be seen that the number grows exponentially(appears linear in log scale) with respect to the mismatch cost.
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 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 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
Page 84 and 85: % Error Fig.4.5 A comparison of the
Page 86 and 87: 0 I 1 I 0 10 20 30 40 50 60 % Error
Page 88 and 89: 60 4 0 No. of subsentences No. of s
Page 90 and 91: LC1: A Hindi word can end either in
Page 92 and 93: Fig.4.10 along with the results for
Page 94 and 95: 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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