A Rule Induction Approach to Modeling Regional Pronunciation Variation

This 1)~q)er descril)es the use of rule induetion techniques fi)r the mli;omatic exl;ra(:l;ion of l)honemic knowledge mM rules fl'om pairs of l:,romm(:intion lexi(:a. This (:xtra(:ted knowledge allows the ndat)tntion of sl)ee(:h pro(:essing systelns tO regional vm'iants of a language. As a case sl;u(ty, we apply the approach to Northern Dutch and Flemish (the wtriant of Dutch spoken in Flan(lers, a t)art; of Belgium), based Oll C(?lex and l'bnilex, prommclarion lexi(:a tbr Norttmrn l)utch mM Fhm,ish, r(~sl)e(:tively. In our study, we (:omt)ar(~ l;wo rule ilMu(:tion techniques, f ranslbrmationB;tsed Error-l)riven Learning ('I'I/E])I,) (Brill, 1995) mM C5.0 (Quinl~m, 1993), and (,valuate the extr~tct(xl knowh;dge quanl:it~l;ively (a(:(:ura.cy) mM qualitatively (linguistic r(;levanc:e of the rules). We (:onchMe that. whereas classificntion-1)ased rule. induct;ion with C5.0 is 11101.'0 a(;(:(lr&l;e~ th(? |;rallSt~)rnl;~l;ion l"ules le;~rne(t with TBE1)I, can 1)e more easily ini;ert)reted. 1. I n t r o d u c t i o n A central (:onq)onenl; of speech l)ro(;essing systems is a t)rommciation lexicon detining the relntionshi t) between the sl)elling mM t)rommcin|;ioi1 of words. Regionnl wMants of ~ langut~ge may differ considerably in their l)ronunci:ttion. Once a spe~ker from a particular region is detected, speech inlmt and output systems should be al)lc to ~Mat)l; their t)rommei;Ltion lexi(:on l;o this regionM vm'bml;. Regional l)rommciation (litiin'ences are mostly systeln~ti(: mM can t)e modeled using rules designed by experts. However, in this 1)at)er, we investigate the :mtoma* This resear(:h was l)artially funded 1)y the. F\V() 1)reject Linguaduct and the i\VT project CGN (Cortms Gesprokcn Nedcrhmds). tion of this process by using data-driven ted> niques, more. specitically, rule induction techniques. l)ata-(lriven reel;hods have proven their effi(',;tcy in severM language engineering tasks: such as gr~l)hemc-to-tfl~oncmc conversion, tmrt;of-sl)eech tagging, el;(:. Extraction of linguistic knowledge, fl'(nn a snmple corlms instead of numuM encoding of linguistic intbrmation proved to be ml extremely powcrflfl method tbr overcoming the, linguistic knowledge acquisition bottlene(:k. ])itt'erent at)preaches are awfilM)le, such as decision-tree le~rrning (l)ietterich, 1997), lleural lml;work or (:onne(:tionist al)proaches (Sejnowski ~tnd l/.os(ml)erg, 1987), lnemory-base(1 lena'ning (Daelemans mM van den Bosch, 1996) el;(:, l)at~-driv(m al)i)roaehcs (:~m yield (:Oral);> ral)le (;111(t often eVell better) results ttum the rule-lmsed at)t)ro;mh, as described in the work of l)aelemans nnd wm den ]~os(:h (199(i) in which a (:omt)~rison is mnde 1)ctwe(m Morpa-cmnMorphon (Heemskerk and wm He, uv(m, 1993), an ex:mlt)le of n linguistic knowledge 1)a.sed at)1)roacll |;o gr~t)heme-to-1)honem(~ (:OllVersion and [G-'.lh'ee, an examph; of n m(mloryd)ased at)1)roach (Daelen~ms et M., 1996). Ill this study, we will look tbr the patterns and generalizations in the i)honemic ditrer(m(:es 1)et;ween Dutch and Fhmfish 1)y using two (tat;ndriven t(~chniques. It; is our aim to extract the regularities that are implicitly contained in the data. Two corpora were used tbr this study, r(~l)resenting the Norl;hern Dul, eh and Sout;hern Dutch w~rbmts. D)r Northenl Dut(:h Celex (releas(; 2) was used and for Flemish Fonilex (versioll 1.01)). The Celex datM)ase contains fiequen(:y infi)rlnation (based on the INL corl)uS of the hlsl;itute fi)r 1)ul;(:h Lexieology), and i)honologi(:al~ morl)hologicM , and synt;a(:tic lexicM intbrmation tbr more l;tmn 384.000 word forms,