W12-4809 the system can only perform the forward transliteration of Text to Braille . In future
W12-4809 , we have constructed distinct forward transliteration rules for Dzongkha text to Braille
N07-1046 . In this paper , we focus on forward transliteration from Arabic to English . The
W12-4407 of FSA . Table 3 shows that the forward transliteration performance gets consistent improvement
W11-3212 recognizers . We also train another forward transliteration consisting of 20 recognizers
W09-3519 table 4 . Experiments show that forward transliteration has better performance than back
W09-3519 tuning , among which the ACC of forward transliteration gets improved by over 11 % .
W11-3212 other numbers in backward and forward transliteration during NEWS2011 . Because the
W12-4809 automatic Dzongkha text to Braille forward transliteration system . Dzongkha is the national
W12-4809 PRO , Romeo and Juliet Pro . 4.2 Forward Transliteration Based on the information discussed
P97-1017 useful for English-to-Japanese forward transliteration . 3.4 Japanese sounds to Katakana
J98-4003 useful for English-to-Japanese forward transliteration . 3.4 Japanese Sounds to Katakana
E97-1017 useful for English-to-Japanese forward transliteration . 3.4 Japanese sounds to Katakana
W98-1005 algorithm at IBM for the automatic forward transliteration of Arabic personal names into
P07-1119 AbdulJaleel and Larkey ( 2003 ) model forward transliteration from Arabic to English by treating
W12-4809 training data is available . <title> Forward Transliteration of Dzongkha Text to Braille </title>
W09-3519 Experimental setup Both English-Chinese forward transliteration and back transliteration are
W09-3507 et . al. , 1994 ) . They model forward transliteration through a combination of neural
W09-3537 problem of automatic English Chinese forward transliteration ( referred to as E2C hereafter
P09-2006 problem of automatic English-Chinese forward transliteration ( referred to as E2C hereafter
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