A Generic Framework for Spoken Dialogue Systems
and Its Application to a Car Navigation Task
Y. IionoT. Yanohl. Sasajinla

X:ono@X:~-l.toshiba.co.jl~
Iiansai Research Center
Toshiba Corporation
8-6-26 hlotoyama-hlinami
Higashi-Kada
Iiobe 658-00 15, Japan

yano@krl. toshiba. co.jp
Iiansai Research Center
Toshiba Corporation
8-6-26 hlotoyanla-hlinam~i
Higashi-Nada
Iiobe 658-0015. Japail

sasa@krl.toshiba. m . j p
Iiansai Research Center
Toshiba Corporation
8-6-26 kfotoyai~~a-hliriami
Higashi-Kada
Iiobe 658-0015, Japan

Abstract
This paper presents E U R O P A , a n e w genericframework for spoken dialogue systems, and its upplication to a car navigation task. A EUROPA-based
system is based on keyu~ord-spotting. i.e.. it accepts
and understands user's utterance as a set of keywordsequences, to cope .with the diversity of urording i n spoken lanauaoe.
., ,.
Applying voice interface techniques to practicaltasks such as car naz~iyationinoolues m a n y recognition
errors. false alarms i n the case of key.uiord-spotting.
EUROPA employs n neur parsing algorithm. B T H ,.uthich is capable of e.@cien.tly parsin.g a key-u~ordlattice that contnins a large number of false a1arm.s. Th.e
B T H parser runs .urithout unfo1din.g the given key.utor(l lattice, and thus it can eficiently obtain a setof key-word-seq.uen.ces acceptable to the given yrammar
as the parser result.
This paper also presents M I N O S , a prototype spoken dialogue system uihich is bwilt by applying EUR O P A to a car n.a.iGyation task. It understan.ds theuser's spoken queries o n either places or sections. andreplies to them by synthesized voice. R r t ~ n so n a single Win.douis-based portable PC i n near real time.

1

Introduction

Car navigation with voice interface is clesiral>leforaiding cliivers ~vhoseeyes. ha~lds,and legs are employed for cliiving. Recently. sonle car navigation systenls are equippecl with not only guidance generation
with synthesized voice. 11ut also with operation acceptance with speech recognition. However. voice inteiface is not po~)ularsince it is neither useful nor
user-friendly for the typical driver. -1voice interface
to/with ~vhicha user is able to -talk" as if to/\vith ahuman is ~leeclecl.This paper foc~iseson the following
three points.
First. the voice int el-face sho~ildaccept spoken Ianp a g e . Application of the voice interface is effective
especially when the user cannot use his/her llands because of another task. for esanlple. driving a car, cooking in a kitchen. or operating a power plant. It ~vould

be difficult for such users to speak their intent,ion ir
written language. ~vhichinvolves many gru~lnlat.ica.
constraints.
Second. it is very important for developing gooduser interfaces to shorten the duration between users'
tests. their feedback to the interface. ant1 nest tests
T h e more tests. the better the interfaces will be.
Lastly, the system \\-it11 the voice interface should
answer users' questions within a short time. So far.
nlost car navigation systenls \\-it11 a voice interface r ecluire prompt ansn-ers. Slow systenls are not usefulIVe haye developed BTH. Blln (meaning '.sentence" irJapanese) Template Hash. a passing algorithm whicl
is capable of efficiently parsing a key\\-orcl lattice wit1
a large nunlber of false alar~lls[l].
Employing the BTH parser. me have developet
a new generic frartle\\-ork for spoken dialogue sys
tems. called EUROP=\ (Environment for l>l~ilding
Utterance Recognizeable P-1ckage). which is a packageof modliles that makes possil~leeasy attachment oa voice intel-face f~inctionto task-depentlent system-4 EL-R OP.4-lmsetl system accepts ;ultl ~intlerstand.
the 11wr'\ Iitterxlce \I-hich coiltaills 11011-graninlatica
ternls. iinilecessary ~vortls.etc. ;uld replies to it in synthesi7etl \ oice. EUROP.4 is applietl to protor! ping o'a car navigation systrm. call~tl5IISOS. It answer
two types of cl~iestions.One is a locatioll of a thin:such as a facility, a service-area. a parking lot or 2
shop. The other is a dl~rationbet\veell two locatio~ls.-\I1nlocl~ilesare bllilt on one notellook PC(Penti~un1
3GGAIH7). ~vhichaccepts more tllan 1 niillion pattern.
of sentences and in almost all cases aiswers within 1
secontls.

2

EUROPAIYe have clesignecl EUROP.1 as a generic frunen-ark
for spoken clialogrie systems. Figlire 1 slloiv, the overall process of man-machine clislogie in EVROPE. Thfuser's iitteratlce is recognizetlI>y the key~vord-spottin:
engine anrl it generates a keyn-ortl lattice as a r e c o ption resiilt. The ol>tdnedlattice is parsec1 1)y the BTF
parser to generate a set of possihle key~vorcl-seclriencesFigure 1: Dialogue process
Figure 2: Overview of hEYOS
each of \vhich is acceptable by the given grrunmar. The
set is analyzed and solved in ternls of meaning and t,henlost plausible keyword-sequence in the set. is selected.
other consists of domain-independent modules such
Finally. the reply to the user's question is played backas a lattice parser, an interpreter for translating user
by synthesized voice.
intentions, and an interpreter for generating answer
To accept spoken language. our voice recoglition
sentences.
moclule does keyword-spotting. The seqrlence of spotTo rlm a dialogue. the dialogue system must conted kq.11-orcls represents the user's intention. 111 t,hetrol a set of modules which belong to one of the twocase of spoken Japanese. misuse or loss of partigrollps. For esample. a car navigation task whichcles often occrlrs. Iiey~vorclspotting does not deal
solves a location specified by a user utterance, re~vi
t 11 r hem. \vhicll simplifies accept able grrunmar rules.
quires not only a generic parser hut also a domainThis c11.x-acteristicalso solves the proble~llsof dealing
specific prol~lemsolver ~vhichresolves the location the
nit11 unnecessary terms such a5 %ah" or -\veil." Just
Ilser intended. Embedding such control codes includthen1 fro111the keyword set. we can acby escl~~rlinging clomain-specific parts reduces portability of the
cept sentences wit11 these \\-orcls. F ~ ~ r t hmore.
erchangefriunen-ork. EUROP.4 solves this prol~lemby adoptof wort1 order ~vhichalso occurs in Japanese spoken
ing an interpreter for such codes that manage cliadialogue can Ile dealt with easily. The BTH parserl o g ~ ~ e\Ye
. call this interpreter "USHI" which stands
is employed for efficiently parsing the key\\-ord latticefor "cilificatioil-basecl Script Handling Interpreter."which is ol~tainerlby keyword-spotting. allcl it. is t.rans-4 sysrenl developer clescril>esthe process of meaning
forlllcd into a set of possil~lekeword-sec~uences.The
analysis. prohlenl-solving. and response generation indetail of tlle BTH parser is tlescril,etl in Section 3.
an L-SHI script. ant1 the mod~ileinterprets the script.
O l ~ t a i n i ~ ithes set of possil~lekey~vortl-sccl~~e~~ces
To gain ~ n o ~ l gspeed
h for the response. the USHI scriptfrom rllc BTH passer. the clialog~~econrrolling mod11le
for nlanaging prohlenl solving is compiled beforehand
proce.s:,tssit to generate a reply. The overall configuinto another forin to be interpreted faster as shown inration of the moth~leis depictecl in Figure 3. Firstly.
Figure 3.
it triulsfor~listhe given set into a set of representaintention.
teach of ~vhichcorrespollds totions of i i l p ~ ~EUR0P.A is applied to prototyping a car navigationa key\vorrl-seq~lencecantlidate. by employing Intensystem. called SIINOS. Figure 2 shows the overview
tion Triulslator. Then it resolves a user's question byof it. -4 sample screencopy of hIINOS is also shownreferring to the kllo\vlerlge ba.se and generates meanin Figure 5 . The system answers two types of quesh
toing representation of rhe clilery ~ v l ~ i ccorresponds
tions. One is a location of a thing such as a facility.
t.he nlost pla11si1,le key~vorrl-seclr~tllre
ancl its ;uls\\-er.
a service-area. a parking lot or a shop. The other is a
Obtaining the infor~iiation.Response Cellerator genrluration between two locations. -411 modules are built
erare:, respontling sentences. The problem-solving proon one note1,ook PC(Penti11n1I1 26G;\IHz), ~vhichaccess is I,asecl on l~nificationof f e a r ~ ~stli~ctt~res.
recepts more than 1 million patterns of sentences and in
To shorten prototyping and testing cycle ELTROP.4
allnost all cases answers within 2 seconds. In selectseparates 1nod111esinto two groups. One consists of t.heing seiltences to answer fro111input lattice. the systemdomain (Iepenrlent ~ n o d ~ l l suche s as a ~vordclictionary.
refers to their scores that are calculated in terms of@;l.arnnlar rules, r111es for translati~iguser intentions.
phonetic val~ieand meaning. One more function ofrules for generating sentences as an answer to the user
ELrR0P.A is asking hack to the user \\-hen the score
i n p ~ ~ultl
t , donlain-specific prohlem solvers. such asof the sentence is lower than a given threshold suchPlace-espressio~lResolver ant1 R o ~ ~ Resolver.
teThe
as .'Did yo11 ask ahollt a resta~lrantjust before theI

I

Dialogue Controlling Module

USH1:Vnficalim-badScrip1 HandlingInsLNcGm ICI

Figure 3: Overall configiration of the dialogue controlling nlodlile
destination?"

3

The BTH Parser\Ve had previously de\-elopecl TOSBURG-11. a spoken language dialogue system[l. '71. It picket1 rip theacceptable keyword-sequence as a sentence from the
keyword-lattice obtained from a key\[-ord-spotting engine for the task of selling harnl~lirgerswith a very
limited vocal~ulary.ancl performed the senlantic analysis / intention ~~nderstanclillg.The system recognized only meaningful tvorcls. i.e.. l ~ o ~ i and~ l s verl,s.
received a plalisil~lekey~vorcl-secl~iece
as a sentence.
ancl responded to the iiser 11y ~ n t h e s i z e t lvoice. TheTOSBURG-I1 stlicly revealed the follon-ing: (1) 11y
applying keyword-spotting tech~lologv. it is possihle
to cope wit11 phenomena characteristic of spoken language such as unnecessary ~ ~ o ror
d sunfixed fornls. (3)false alarms which are unavoidable in the key\\-ordspotting process can be also corrected by sentenceanalvsis.
hi order to expand the scale of the task \vhich a
spoken language dialogue system is capable of esecuting and to apply the system for practical tasks. it isnecessary to correspond to a large vocal~ularyancl alarge-scale grarm1la.r. How-ever. expalsioll of vocabulary and grammar causes the nuilll~erof recognizetl
words and the size of the correspoildi~lgkeyword lattice to increase explosively. This poses a problem inthat the processing time required for analysis becomeshuge.
11-ehave developed BTH. Bun (nleiuling "sentence"in Japanese) Template Hash. a parsing algorithm
which is capalde of efficiently parsing a key\vorcl-latticewith a large ~l~ilnl,erof false alarnis[4]. \Vhen a keyword lattice is large. the list of possihle keywordsequences generated by \infolding the lattice is hugetoo. Parsing the culdiclates in the list one by
one calises an explosion of calculation time. The
BTH parser nins wit holit ~infolrlingthe given keyword lattice. This parser (1) holds hash tables foreach sentence-type template. ( 2 ) calculates the set ofsentellce-types to whicll each ~lotlein the lattice canI~elollgby referring to the talde. and ( 3 ) propagates
the list to the notles follo~vingeach norle. It can efficiently o l ~ t d l al set of key\\-ord-seclllence~acceptable
to the given granlniar as the parser result.
\Ye have illlplenlelltetl this algorit,llul on PCs andtested the method by applying it to a car navigation
task. In this work. the vocabulary for recognition esceetls TOO words. and the keyword lattice that can generate over 1,000,000 cultlidate key\vortl-setluences 1s
usually obtained rrs a recognition result. Also. parsing~
of slich a large-scale lattice c a l ~ l s u d l yI I colrlpleted
in real time.
Olir research goal is to develop a generic frame
work for spoken interfaces that rtuls on practical applications lvith coilsideral~lescale of task. \\e cannot
expect a 1OO'A. recoglit ion rate \\-it11 current speech
recognition technologies. even if strong constraints areapplied as a langliage ~llotlel.It is especially difficultdi.e.. \vosds thatto correctly recognize b o ~ i ~ lwords.
have no nleanings tllelnselves. e.g., postpo.sitions. Wehave tlesignecl 011s speech u~lderstandingmechanism
based on the following policies:.
.

Embodying scalability so that it works well in
1xact.ical applications.
.ln;llzing/runtlerstmdillg a user's spontaneous~itteralcewith the grammar that accepts a sentmce as a keyl~-ord-se(l~ie~lce
consisti~lgonly of
free words. i.e.. ~1-0rdsthat have meanings themselves. e.g.. nouns and verbs.

r Recognizing a user's utterance by the keyword-spotti~lgtechniclue. .A list. of possible keywordsequences is ohtainetl by parsing a key\\-ord lattice
result.
generated from a ke~~vord-spotting
To realize our god. it is crucial to establish thet e c 1 ~ i i a l ofor
~ post-recognition process that can efficie~ltl>extract plausible sentences/key\vord-secluencesby tlealing wit11 the keyword lattice ohrained from the
keY\vortl-sp~tting engine. I\-e have tlevelopecl the BTH
passer \vhicll is capable of efficiently parsing a keyword
lattice that contairls a large number of false alarms[4].
This section describes its features ant1 techniclues.

3.1

Keyword-Lattice Parsing Problem

One techniql~efor parsing a lattice structure thatis obtained hy speech/hand~vriting recognition is tosecl~ientiallyapply a natliral language parser to candidate sentences one by one ~vhichw e generated by unfolding the lattice. The TOSBURG-I1 employed a generali7rd LR I)arsrr[3].which iu~al?.zesa key\vorcl latticegeneratin.. nlicl\vay caxitlitlates and prlil~inghopelesspatl~s[l. The key~vordlattice. however. generated
by a speech recognizes with a large volu~neof vocabular?. ant1 griunnlar. contains a complicatetl structure
wit11 many nodes an(] fonns in general. It is hard toanaly7e each cantlidate generatetl by unfoltling the lattice in the liaht" of both cdc~llationt i ~ n eand memom.
It is essential to retrieve a 1111geanlolint of both vocal~ilaryand gsa1lIn;u- to tlevelop a speech interface
for a 1)ractical application. I h collectetl 389 example
S ~ I I ~ ( W C Flittrr;illce\.
rhar are narr~raland needed by
drivrrs who Ii<e car navigation 5~-stems~vithspeech
recognition. They illcl~itleactllal utterances made by
one of 11, ti-liile clriving a car and \vhile being a passenger in a car. T h m we alalyzetl them and made granlmatical r~ilcsthat apply to those esiu~lples.11-e also
designed stnictlires of the user's intentions 13y classifving the collected tirterulces into 5lY (IYhere. II-hat ,iyhich. IYhen. IYhy) and 2H (How. How-lludl).
In parallel with the analysis of sentence patterns
ant1 grammar. we 1)icketl up keyn-ortls froin the esample ut t prances. For instance. tlie sentence "Deguchino-mae-110-s;Jgo-no-service-area-ha-doko?
(IYhere is
the last service x e a before the exit'?)" was classified into the seiltence-t~pegro~ipof ..lYl~ere."and fiveI\-ortls. i . ~ . . tleg~lchi (esir). lnae (before). saigo (thelast). service-area. ant1 tloko (I\-here).were selected as
key~vords.-As a result of the analysis. about TOO keyxvords \\-ere picked up as recog~litionvocal~ularyand
were classified into al)out 110 wortl-classes (parts of
Speech).

q.

-4s the vocabulary for speech recognition becomeslarger, the number of false alarms in a recognition
result increases and the corresponding keyword lattice becomes huge. Figure 4 depicts the exarnple keyword lattice which is obtained as the speech
recognition result when a user uttered the sentence
above ('Deguchi-no-mae-no-saigo-no-service-area-hadoko?"). 36 key\\-ords. i.e., 3 correct and 31 false
alarms. \\-ere spotted in this example. IYhen only connectable time constraints are applied, over 4 million
possible k e y w o r d - s e q u e c can be generated, each ofwhich is a different path from the start node t o theend node, if the lattice is unfolded. Thus a latticeparser, which is capable of parsing without unfolding
a given keyword lattice and generating a set of acceptable keyword-sequence candidates by given grammar,
is required.

3.2

Parsing a Keyword-Lattice with Temlate Hash Tables

Based on the following viewpoints, we have developed the BTH (Bun [meaning "sentence- in Japanese]
Template Hash) parser. which is an efficient latticeparser for keq.11-ord-spotting that satisfies the abovementioned recluirements:
Real-tjme processang an the scale of practical applicatrons using conzlentional calculation power:
Both more vocabulary and less calculation are required to develop a practical speech interface. We
aimed to realize a parser working in real-time on
700-800 MIPS RISC hlPUs. i.e., the h P U s thatare expected to be applied in next-generation car
navigation systems.
Grnmmattcal representntzon taking into accountboth the recognotaon method and the apyltcation:
It is difficult to construct a comprehensive and accurate grammar for actual utterance even if thetask to which it is applied is limited. because\\-ord order and syntax in actual utterance are
11-ide-ranging. A weakly constrained grammaticalrepresentation is req~liredto cope with noticeable
phenomena in speech. s ~ i c has inversions. On theother hand. it is poilltless for the parser to accept
sentences ~vllichthe application program cannot
execute. -4 granlmatical representation that generates very conlplicated sentences. e.g.. one employing recursive rules, ~vouldbe too expressive
for practical use.
Pnrs.ing urithout unfolding the ginen lattice: In thecase of a large vocabulary. a recognit.ion result.
lat tire from a keyword-spot,ting engine includes
many false alarms as nientiorled above. It costs
ht~gecalculation time if the parser analyzes unfolded candidates one by one. Therefore. a l a b
rice parser. \I-11ich is capable of parsing n-ithout.
infolding a given key\vord 1at.tice. is required.

The BTH parser elnbodies the following feat.ures:
Grammatical representation based o n templates
of ulord-class sequences: The gramnlar is represented a s a set of templates. each of ~vhichforms

Figure 4: An example of a keyword latticea word-class sequence. .in interface designer collects sentences in a task and classifies them into
sentence-types. i.e.. word-class sequences. The
parser analyzes a keyword lattice utilizing thetelllplate sets. The grammar nllist be rqxesented
11011-reciirsivel?:
Generation of a h a s h table from the s ~ n t e i l c e - t y p etentplate: -4s a preprocess of the parsing. thegiven sentence-type template is compiled into ahas11 table. \\-hose elelllent is a correspondence
between the appearance of a certain n-ord-classin sentences and the established set of sentencetypes \vllich applies to the ~vord-cla5s. X Bun(meaning "sentence" in Japanese) Template Hasht a l ~ l eis conlposecl of representations. each of\vhich means -if a worcl of a certain n-ord-classis recognized at a certain order in a sentence.
the word of the order applies to either of certain
sente~lcetypes."
Parsing of a lattice by propayrrtrny belonytng
sentence-type tnformatron: The passes scans t l ~ egiven lattice from the start node. .it each nocle.
the set of sentencetypes. to \vllich a certain notle
in the lattice belongs. is calclilated 11y esamining set calccllations I>et\veenthe corre.spontling e1ements in the hash table and the set of sentencetypes propagated from the llotles connected justin front of the notle. Then the set is propagatedfrom the node to connecting node(s). By repeating the nlethod. the set of sentence-types to \vllich
the given lattice helorlgs is finally ol)t ained.

The detailed parsing process is tlescribed iu the llest
subsection.

3.3 Parsing Process
To parse a keyword lattice, the BTH parser utilizes
fo~lrkinds of information stnlcture. i.e.. a tlictionq
of word-classes allrl ~vorcls.a dictionary of patterns ofworrl-class seclllences. a sentence- type Ii,zsll rlictionary.
and a list of processing lodes. The list of 1)rocessing
nodes is the set of nodes that are the current targets
of processing. Each node ill the lattice holtls threekintls of data. i.e., a list of antecetlent lodes. a list of
unprocessed antecedent lodes. and an inter-processing
list, composed of sets of sentence-types. each of whichis the sentence-type to ~vhichthe node belongs at a
certain order.
Given a new lattice to analyze. at tlle beginningof tlie l>arsing process. the BTH parser (1) copies
the list of a~itecetlent nodes to the list of unprocessctl a~trcetlentnodes for each node in tllr lattice.
( 2 ) adtls dl the nodes \vhich may possiI)ly Ile headsof reuilt k q . \ ~ o r t l - s e c ~ ~ ~ eto~ ~thec e slist of processing
nodes. and ( 3 ) sets the initial score that i5 ol~tained
from the sentence-type has11 clictionary for each interprocessing list of the node in the list of ~rocessing
nodes.
Then tlle passer repeats the propagatioll process
while the list of processing nodes is not elllpty. In the
propagation process. the folio\\-ing steps ase applied toeach node i11 tlie list of 1)rocessiilg no~les:1. Tdie one notle as the crirrellt target from the ljst
of processing ~ ~ o t l ea si d check n-hetl~erthe listof unprocesserl antecetlent notles is enll)ty. If it
is not empty. check the nest 11ode in tlie list ofprocessing nodes.

2 . If the list of unprocessed a~~tecedent
norles of anode S in the list. of processing ~loclesis en@!:propagate the inter-processing list. of 1 to id1 sue-c~eclingnodes and add them to the list of processing nodes, and then remove S fro111 the list..

3. =\t each node to which the inter-processing listof its anbecedent node is propagated. re-calcu1at.e
the inter-processi~lglist.
of processing nodes is empty when step 3
is conlpleted. the inter-processing list of the end
llode is the set of acceptable sentence-types by thelattice. The parser result. i.e.. the set of acceptable~ev~vord-secl~lencesby the lattice. is obtained from theset of sentence-types by re-scanning the lattice.
The cost of the entire parsing process can be estinlate~lfrom the cost of set calculations to obtain theinter-processing list for each node. The masimum possible nunnber of set calculations. i.e.. unions and intersections. is equal to the number of links in the lattice
times the number of words in the longest keywordsecluence. It is tlie worst case. ho\vever. Rluch lesscalculatioli is recl~~irecl
in nlost cases.
~f tile list

4 . MINOS

Figure .5: Sample screen of h m O S

5Concluding Remarks

lye have designed a generic framework for spoken
dialogue systems. EUROP.4, which employs an effiCVe have implemented a spoken dialogue system
cient keyword lattice parser. BTH, and developed abased on ELROP-4. called XIIWOS. which is able toPC-based spontaneous speech interface for a car navirespond to queries respecting locations made by a user
gation task. SLINOS. hlINOS processes over 700 words
while driving a car. hlodules in hIIXOS run collaboof recognition vocabulary. hIINOS is able to respondratively. comniu~iicating\\-it11 each other via socketto
a user's question within a few seconds of the quesbased niessaging. -4s shown in Figire .5. the application being asked on a portable PC.
tion mocll~leof hIINOS has a map-based appearance.
In the case of the task. it is conlrnon for overwhich is hased 011 ProXtlas. a nlap soft\\-are developed
100 spotted words to be notified from the recogniby -4LPS Mapping Co.. Ltcl. It simulates car dxdrtion engine. and consequently over 1 million possiing by re-playing pre-recoreled position data obtained
ble keyword-secluences can be generated by unfoldfrom the Global Positioning Satellite (GPS) system.
ing the corresponding lattice even if \\-ord-class biThe application module simulates drivi~iga car
gram is applied to the lattice. Our method. ho~vand continuonsly notifies the dialogue nlodule ofever. is able to parse such a large lattice in a practical
the simulated current position at reglilar intertime. The result shou-s promise respecting implemenJYlien a user asks a question invals of time.
Japanese. e.g.. "Dealchi-no-mae-no-saigo-no-service tation of the function of spontaneous speech interface
in nest-generation car navigation systems ecluippedarea-ha-doko? (JYhere is the last service area bewith RISC IIPUs of about 7OOSLIPS.
fore the esit?)" tlie keyword-\potting engine recognizes the iltterance and notifies the dialogue controlReferences
ling mocl~ileof a \\-ord lattice as depicted in Figire[I] Takebayashi. J-.. Tsuboi. H., Iianaza~va, H.,
4.
Sadanoto. Y..Hashinloto. H.. and Shinchi, H.. "AhIINOS an;ilzes the I\-ord lattice I)! eniploying theReal-Time Speech Dialogue System Using SpontaBTH parser ant1 obtains a set of candidate key~vordneous Speech Unclerstanding.' IEICE Trans. Inf.
sequenceh. ~vliicllis sorted in the descending order of& Syst.. E7G-D: 112-120. 1993.
the initial score of each candidate. \\-l~ichis calculated
from phonetic scores of u-ords of the candidate. Each[2] Takebayashi, Y.,Tsuboi, H., a i d Iianazarva, H.,
keyword-secluence candidate is converted into a user's-1ieyword-Spotting in Noisy Continuous Speechintention in the form of a typed feature structure. andUsing I[-ord Pattern lhctor Subabstraction andis resolved by using cilrrent position information andNoise Immunity Learning." Proc. ICASSP '92, IIthe knolvledge I ~ s e The.
knolvledge base is a semantic
8.5-88. 1992.
network that contains all the knowledge reeltiired tosolve questions itimut locations 011 the clisplayed map.
[3] Tomita. II., "An Efficient Jibrd Lattice Parsing
The score of each canditlate is revised taking acXlgorith~iifor Continuous Speech Recognition."count of the cost of probleni solving. ancl tlie list of
PTOC.ICASSP '86, 1.569-1372. 1986.
the candidates is reordered. .As a result. the candidate[4] Iiono. J'.. Yano, T.. and Sasajima, hl.. "BTH: An\\it11 the Iwst score is selected and the test of the anEfficient Parsing Algorithm for C%-ord-Spotting."
swer correspontling to tlie cl~iestionis generated. e.g..
PTOC.ICSLP '98, 2067-2070, 1908.
".4magasaki Service -4rea is the last service area before the Nishinomiya Intercliange." and the dialogue
colltrolling nlod~ilenotifies the syntliesizer of it.

.