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TARSU- Persone Fisiche *. Italian fiscal data entry system for waste management taxation [email protected] (*) eng. TARSU-Physical People TARSU = Tassa Raccolta Rifiuti Solidi Urbani. Background. The current taxes office inherited their databases on taxpayers from other offices

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tarsu persone fisiche

TARSU-Persone Fisiche *

Italian fiscal data entry system for waste management [email protected]

(*) eng. TARSU-Physical People

TARSU = Tassa Raccolta Rifiuti Solidi Urbani

background
Background
  • The current taxes office inherited their databases on taxpayers from other offices
  • They are affected by errors or missing inf.
  • Commune of Rome is willing to purify them
  • Staggered mailing to targeted sections of their taxpayers
  • The taxes office is using an existing service, developed by Loquendo, for business premises
  • It is a DTMF system as the access key is the VAT registration number (only digits)
fiscal data entry system
Fiscal data entry system
  • Loquendo has developed a new service, voice-operated for private citizens with business not recorded in the public VAT registry (access key: Fiscal code - 16 char alphanumeric code)
  • It uses Loquendo vocal technology (TTS and Speech Recognition)
  • Customers, identified by their own ID (sent them by Rome officer), enter their “fiscal card data” and their “business VAT code”.
speech form used

9989020

ROSSI

MARIO

MI

COLOGNO MONZESE

10 MAY 1963

M

RSSMRA63E10C895A

7310G

Speech Form Used
  • customer ID
  • fiscal card data
  • surname
  • forename
  • city & prov. of birth
  • date of birth
  • sex
  • fiscal code
  • business VAT code

(8 voice steps)

fiscal card data
Fiscal Card Data

Fiscal Code

Surname

Forename

Sex

Birth Place

Province of Birth (code)

Birth Date

example of fiscal code

Rossi

Mario

10th

1963

Cologno Monzese

May

born on the

of

in

R

S

S

M

R

A

6

3

1

0

C

8

9

5

E

A

f

a

b

c

d

e

g

Example of Fiscal Code

Rules are in the next slide

fiscal code rules
Fiscal Code Rules

a) The first three characters are made up of the first three consonants of the surname. If the surname contains less than three consonants the remainder is made up using the first n vowels.

b) As for a) using the first name.

c) The next two characters are based on the year of birth – 1963.

d) This is the code for the month of birth, in this case May.

e) This is the day of the month on which the individual was born. For males, this figure ranges from 1-31. For females, 40 is added to the day of the month so that the range is 41-71. In this way gender can be distinguished.

f) This is the code for the city of birth, in this case Cologno Monzese. The city code list is maintained by Finance Ministry.

g) This is a checksum character.

business vat code
Business VAT code
  • It is an alphanumerical code, designed by Finance Ministry to identify univocally all business typologies (e.g. pizzeria, university, hospital, shoes maker, ...).
  • Currently more that 1232 codes have been defined.
  • They are variable in length and composition:
    • 4/5 digits or
    • 4 digits + 1 letter
business vat codes examples
Business VAT codes examples

4067 PRODOTTI NON ALIMENTARI, NON ALTROVE CLASSIFICABILI

9262C ALTRE ATTIVITA\' PROFESSIONALI SPORTIVE INDIPENDENTI

26620 FABBRICAZIONE DI PRODOTTI IN GESSO PER L\'EDILIZIA

31621 FABBRICAZIONE DI ALTRI APPARECCHI ELETTRICI N.C.A.

52626 COMM. AMBULANTE A POSTEGGIO FISSO ART. DI OCCASIONE

7310G RICERCA E SVIL. SPERIM. NELLE SCIENZE NATURALI E INGEGNERIA

9262A ATTIVITA\' PROFESSIONALI SPORTIVE SVOLTE DA ATLETI

the service structure
The service structure

START SERVICE

CONN. DIGIT GRAMMAR

  • GET ID CODE (voice or DTMF)
  • ACCESS DB to GET USER IDENTITY
  • PROMPT USER IDENTITY
  • GET IDENTITY CONFIRMATION

YES/NO GRAMMAR

  • GET ITALIAN NATIONAL. CONF
  • GET GENDER

DATE GRAMMAR

  • GET BIRTHDAY

ITALY

DATABASE

  • GET PLACE OF BIRTH

FISCAL CODE GRAMMAR

  • GET FISCAL CODE
  • GET BUSINESS VAT CODE

BUS. VAT CODE GRAMMAR

  • RECORD VOCAL SIGNATURE

END SERVICE

customer identification
Customer identification
  • TARSU-PF works only for registered users. They receive, by surface mail, their unique identification number from the Rome Municipality.
  • Upon entering the 7-digit ID code, the system replies with customer “name + surname” as recorded in the database.
  • If the customer DISCONFERM it, the call is transferred to the operator.

CONN. DIGIT GRAMMAR

  • GET ID CODE (voice or DTMF)

YES/NO GRAMMAR

  • ACCESS DB to GET USER IDENTITY
  • PROMPT USER IDENTITY
  • GET IDENTITY CONFIRMATION
language and sex identification

GET ITALIAN NATIONAL. CONF

YES/NO GRAMMAR

  • GET GENDER
Language and Sex identification
  • The TARSU-PF speech recogniser is designed only for Italians. In order to discard foreign people, the system asks for user nationality

“Are you born in Italy?”

  • The next step the system asks for customer gender, with another Yes/No question

“Are you a male?”

birthday recognition main task
Birthday recognition(main task)
  • The system asks to enter the customer birthday, that can be uttered as a whole sentence, using different styles:
    • 10 Maggio 1963
    • 10 5 63
    • 10 05 1963
    • 10 05 63  this is the fiscal card style
  • After the speech recognition, the system asks for birthday confirmation

DATEGRAMMAR

  • GET BIRTHDAY
birthday recognition redo task
Birthday recognition(redo task)
  • In the case of error, the system switches to a block mode, asking day, month and year as separate entries. The previous grammar is re-used and each piece of information can be entered using the same different styles.
  • Each piece of information is explicitly confirmed
  • GET DAY OF THE MONTH

DATEGRAMMAR

  • GET MONTH
  • GET YEAR
place of birth collection db driven speech recognition

ITALYDATABASE

  • GET PLACE OD BIRTH
Place of birth collection(DB driven speech recognition)
  • This task uses a DB driven approach to speech recognition
  • The relational database contains about 20.000 records to correctly identify the 13.600 city names defined since 1861
  • It uses 2 vocabularies derived from the relational database:cities names (+ alias) & district+region (+ alias). Tree steps

1: city acquisition

2 (opt.): in the case of more records selected, the system asks for the additional district or region name

3: in the case of still more records, the system enter in a confirmation loop and the selected list is browsed: than users select the right record.

er model of the italian administrative database

ALIAS_COM_ID

ALIAS_REG_ID

PROV_ID

REG_ID

COM_ID

PROV_ID

COM_ID

NAZIONALE

NOME_COMUNE

PROV_ID

COM_ID

REG_ID

NOME_REGIONE

NOME_PROVINCIA

REG_ID

ALIAS_NOMI_COMUNI

ER model of the Italian administrative database

NOME_COM_ID

ALIAS_COM_ID

1:n

0:n

NOMI

COMUNI

NOME_COM_ID

ALIAS_COM

1:n

NOME_COM_ID

1:1

COMUNI

1:1

1:1

FLAG_CAPOLUOGO

PROV_ID

1:n

ALIAS_PROV_ID

COM_ID

PROVINCE

1:n

0:n

ALIAS_PROVINCE

ALIAS_PROV_ID

ALIAS_PROV

REG_ID

1:1

1:n

1:n

1:n

ALIAS_REG_ID

0:n

REGIONI

ALIAS_REGIONI

ALIAS_REG

fiscal code recognition

FISCAL CODE GRAMMAR

  • GET FISCAL CODE

R

S

S

M

R

A

6

3

1

0

C

8

9

5

Rossi

Mario

10th

1963

Cologno Monzese

May

born on the

of

in

E

A

city code

surname

name

year

month

day

checksum

Fiscal code recognition
  • The recogniser also accepts numbers in groups (e.g. seventy-three) as well as char by char.
  • The recognition task uses the checksum to prompt user with the best correct string, first in the list, for confirmation
business vat code recognition

BUS. VAT CODE GRAMMAR

  • GET BUSINESS VAT CODE
Business VAT code recognition
  • The business VAT code is collected with a three steps procedure:

1) it gets code length (4 or 5 characters)

2) in the case of 5 characters, the system asks if the last char is a digit or a letter

3) it gets the numerical part

4) it get last char, in the case of letter.

  • This approach is long because it is the same already used in the existing system that accept also DTMF. In this case we preferred not to alter the original structure.
vocal signature

RECORD VOCAL SIGNATURE

END SERVICE

Vocal signature
  • The last step records a “vocal signature” from the caller.
  • This is used to certify the collected information.
  • User are requested to record at least their telephone number to be reached by the municipality personnel in the case of errors.
  • This is a “pure” recording task, no speech recognition checks are performed
trial design
Trial Design
  • 100 subjects recruited by a marketing firm company
  • Subjects rewarded with a 30.000 lire voucher
  • Experiment run during 5th – 13th Dec 2000
  • Priming letter prepared by CSELT/CRoma
  • Background questionnaire prepared by CSELT
  • Likert usability questionnaire prepared by CCIR/CSELT
subjects data tasks
Subjects data/tasks
  • Each participant received/performed
    • One priming letter
    • One background questionnaire
    • Two telephone calls
    • Two different tasks: fictitious and own details
    • Two Likert usability questionnaires
dimensions of the subjects space
Dimensions of the subjects space
  • 100 participants
  • 4 dimensional space
    • 2 genders
    • 3 age groups
    • 2 ID code input modes
    • 2 first call orders
  • 2 x 3 x 2 x 2 = 24 different groups selected (average 4 participants each group)
selection of valid data

INPUT MODE

ORDER

GENDER

Age group 1

Age group 2

Age group 3

Speech only

Fictitious 1st

Female

3

7

3

Male

4

6

3

Own 1st

Female

4

6

2

Male

4

7

1*

DTMF enabled

Fictitious 1st

Female

3

7

1*

Male

4

6

2

Own 1st

Female

4

7

2

Male

5

6

3

Selection of valid data

(*) participants removed from the analysis

trial data analisys
Trial data analisys
  • Callers made several mistakes, mainly if elderly and when using fictitious data. Ex:
    • DTMF instead of Voice, or vice-versa, for the ID
    • Fiscal Code instead of ID Code
    • extraneous words: e.g. “My ID code is …”
    • bud spelling: exchanged ‘J’ / ‘Y’, ‘O’ / ‘0’
    • day of the month uttered as whole date
    • too low speaking rate during the FC stage
    • Fiscal code spelled using city names
  • Safe processing
    • No wrong data accepted at all by the system.
attitude by gender own details
Attitude by Gender(own details)

7

6

5

4

negative neutral positive

3

Female N=48, Mean=5.11

Male N=50, Mean=5.28

2

1

Polite

Reliable

Friendly

Efficient

Flustered

Use again

Confusing

Needs

improvement

Frustrating

Liked voice

Ease of use

Voice clarity

Complicated

Under stress

Prefer human

Concentration

Enjoyed using

Speed of service

Knew what to do

Degree of control

attitude by age group own details

< 25 N=31, Mean=5.53

26-60 N=52, Mean=5.09

> 60 N=15, Mean=4.90

Attitude by Age Group(own details)

7

6

5

4

negative neutral positive

3

2

1

Polite

Reliable

Friendly

Efficient

Flustered

Use again

Confusing

Needs

improvement

Frustrating

Liked voice

Ease of use

Voice clarity

Complicated

Under stress

Prefer human

Concentration

Enjoyed using

Speed of service

Knew what to do

Degree of control

fiscal code recognition constrained

False Reject

0.0%

INSIDE VOC.

Substitutions

9.7%

Correct Recog

90.3%

0%

20%

40%

60%

80%

100%

% utts

Fiscal Code Recognition(Constrained)
recognition performance date
Recognition Performance(Date)

100%

80%

60%

40%

20%

0%

ERR %

86.3 %

13.8 %

Full Date

day

month

year 72.7 %

Distrib. Errors

5.6 %

94.4 %

Day only

100 %

Month only

84.2 %

15.8 %

Year only

CORR %

0%

20%

40%

60%

80%

100%

recognition performance city province of birth
Recognition Performance(City/Province of birth)

100%

80%

60%

40%

20%

0%

ERR %

93.7 %

6.3 %

City

90.9 %

9.1 %

Province

CORR %

0%

20%

40%

60%

80%

100%

recognition performance other tasks
Recognition Performance(other tasks)
  • Confirmations resulted practically in100 % correct recognition (the main problem was a 10.4% of TOO EARLY warnings from EPD module, resolved with the repetition of the question)
  • Business VAT code gave about 100 % (both the numerical part than the final letter when present).
summary
Summary

The TARSU-PF service shows that complex fiscal data can really get by voice from the citizens, nevertheless

  • Improvements are necessary to give more context dependent feedback
  • Date grammar must be updated, reducing the years space, to get benefits from the service characteristics
  • Fiscal Code speaking rate from real customers must be measured and used to properly setup the EPD
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