Betz, Martin

Hild, Hermann

English

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LANGUAGE MODELS FOR A SPELLED LETTER RECOGNIZERMartin Betz and HermannHildInteractive Systems LaboratoriesUniversity of Karlsruhe | 76128 Karlsruhe, GermanyCarnegie MellonUniversity|Pittsburgh, USAABSTRACTIn some speech recognition applications, it is rea-e to constrain the search space of a speech rec-rge but nite set of sentences. We demon-a spell ing task, where the recog-ed last names is constrainedque names) of a tele-ss this prob-gepe l -e pl at es ") , \r e -f or i nt er act i ve r ecogni z -names or addr es s e s . I n t he l at t e rr i e s t he s ear ch s pace can be cons t r ai ned t ol ar ge di c t i onar y of wor ds or names . Cons t r ai nt s canbecome eect i ve within the search process as n- gr amsor i n a f ul l y cons t r ai ned s ear ch. They al s o can be us edt o postprocess t he r ecogni zed hypot hes es by mappi ngt hemont o l egal s t r i ngs , or by ndi ng t he hi ghes t r ank-i ng l egal hypot hes i s i n an n- bes t l i s t . I n t hi s paper , wewi l l demons t r at e our l e t t e r r ecogni zer and t he eect sof var i ous l anguage mode l s and s ear ch t echni ques ont he t as k of s pe l l ed name r ecogni t i on. Re l at ed wor k oni s ol at ed l e t t e r s was r epor t ed by Col e et . al . [2].2. THE LETTERRECOGNIZERThe Multi-State Time Delay Neural Network(MS-TDNN) [ 3, 5] i nt egr at es t he t i me- s hi f t i nvar i antar chi t ect ur e of a TDNN and a nonl i near t i me al i gn-ment pr ocedur e (DTW) i nt o a hi gh accur acy wor d- l eve l-unity weights5 time delays3 time delaysFi gur e 1: The MS- TDNN r ecogni z i ng t hwor d `B'. Onl y t he act i vat i ons f orar e s hown.c l as s i er . Fi gur e 1 sof r ecogni z i ng t16 mel s3. EXPERIMENTSETUPe \Tel ephone Di r ect or y" us ed t o cons t r ai n t he s ear chs pace cont ai ned 111, 882 ent r i e s , wi t h a t ot al of 32, 267uni que l as t names . Af t er account i ng f or mul t i pl e pr o-nunc i at i on al t e r nat i ves of s ome l et t e r s , t he nal l i s tof names cont ai ned 43, 181 s t r i ngs , r e f e r r ed t o as t hes t r i ng s et S =fs1; s2; :: : g. The r ecogni zer was t r ai nedwi t h 8, 133 s t r i ngs ( 55, 449 l e t t e r s ) s pe l l ed by 70 s peak-er s . The t es t s e t cons i s t s of 1, 316 s t r i ngs 2 S ( 8, 661l e t t e r s ) s pe l l ed by 23 addi t i onal s peaker s . Swer e s ampl ed at 16 kHz wi t h a Sennhe imi cr ophone . Except f or t hes ame t es t s e t up was u4. BAs a bas e l i ne exper i ment ,wi t hout any l anguaany ot heriin the stmatch):h =h1hi ;The recognitithe n-best l ist. For85%. Saturation occurs at apstring accuracy, as shown in gure 2.(60.7%), the rst-best choice matches an entry il ist. About 5%of these rst-best choices are incorrect.In 5.1%of all cases, none of the n hypotheses has amatch in the dictionary. As expected, the percentageof misrecognitions increases as the rst match occursfurther down the n-best l ist. More detailed statisticsare shown in table 3.position % correct incorrect1 60.7 763 362 10.5 130 83 5.1 54 134 2.4 28 45 1.2 15 16 1.1 14 17 1.3 11 68 1.0 11 29 0.8 9 110 1.1 10 511 - 20 3.6 35 1321 - 30 2.1 19 831 - 40 1.3 11 641 - 50 0.7 7 251 - 60 0.7 7 261 - 70 0.5 5 271 - 80 0.5 4 381 - 90 0.1 1 091 - 100 0.2 1 1none 5.1 0 67Table 3: The histogramshows withwhich frequencieshe best matching hypothesis was foundat various po-he n-best l ist.ULLY CONSTRAINEDSEARCHnstraints were applied aft erated; in this sectionnstraints di-nitewo r d i ni n t he minFSG g r aph,t he l e t t e r E o c c ur s i n ove r 5 , 8 00 t r ans i t i ons . Si nc et he f ul l l e f t c ont e xt o f a s t r i ng i s c ons i de r e d dur i ngt he s e a r c h, e a c h t r ans i t i on may have a di edi v i dua l a c c umul a t e d s e a r c h s cc onve nt i ona l ons e a rW1W2W3W1W2W3timetimeOne list with k actual hypothesesat each frameFi gur e 5 : To p: Conve nt i ona l DTWs e a r c h t e c hni que .The mat r i x c ont a i ns t he pr ohi bi t i ve amount o f 5 7 , 7 13wo r d mode l s , one f o r e a c h l e t t e r i n t he minFSG. B o t -t o m: Two l e ve l s e a r c h wi t h onl y one wo r d mode l f oe a c h l e t t e r i n t he a l phabe t , but an e xpput a t i on o f pa r t i a l s c o rt he a c t i v

Language models for a spelled letter recognizer

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Language models for a spelled letter recognizer

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