Candeias sti lg2p_vfinal

© 2005, it - instituto de telecomunicações. Todos os direitos reservados.
Arlindo Veiga1,2
Sara Candeias1
Fernando Perdigão1,2
1Instituto de Telecomunicações, Polo de Coimbra, Portugal
2Universidade de Coimbra, DEEC, Portugal
STIL 2011
8th Symposium in Information and Human Language Technology
Oct. 14-26 2011 Cuiaba, Brazil
GENERATING A PRONUNCIATION DICTIONARY
FOR EUROPEAN PORTUGUESE
USING A JOINT-SEQUENCE MODEL
WITH EMBEDDED STRESS ASSIGNMENT

2
SUMMARY
• Goal
• Problem Statement
• G2P System
• Joint-Sequence Model
• Stressed Vowel Assignment
• Results
• Conclusions
STIL 2011 - Cuiabá, Brazil - Oct.24-26 2011

3
GOAL
• To Generate a Pronunciation Dictionary for EP
• To Develop a G2P System for EP

4
PROBLEM STATEMENT
What approaches?
How?
Implementing an
automatic system for
converter G2P
• linguistic rules
• Portuguese has an orthography roughly phonologically based 
provides a good coverage of the association between G2P
• No natural human-language satisfies this assumption  the
association between G and P is not quite one-to-one  list of
exceptions
• Very complex, hard and tiresome

5
PROBLEM STATEMENT
What approaches?
How?
Implementing an
converter G2P
• statistics
• Using pronunciation examples it could be possible to predict
the pronunciation of unseen words by analogy
• Is not smart enough…
• vaga -> v „a g 6 vs. vagarosa -> v 6 g 6 r „O z 6

6
PROBLEM STATEMENT
What approaches?
How?
Implementing an
converter G2P
• statistics
• MIXED

7
System based on a mixed approach funded on:
• a scholastic model: joint-sequence model
• rules for stressed vowel assignment
G2P SYSTEM
Alignment between graphemes and phonemes:
“one-to-one”

8 STIL 2011 - Cuiabá, Brazil - Oct.24-26 2011
JOINT-SEQUENCE MODEL
< B r a s i l >
/ b r 6 z i l /
Alignment between graphemes and
phonemes: “one-to-one”

< c h a m o u > < t ê m >
/ S 6 m o / / t 6~ i~ 6~ i~ /
< B r a s i l >
/ b r 6 z i l /

< c h a m o u > < t ê m >
/ S 6 m o / / t 6~ i~ 6~ i~ /

• Implementing the Levenshtein algorithm (“1-01”)
• Defining alternative symbols
• Graphemes  DIGRAPHS
< c h a m o u >
< S a m º >
/ S 6 m o /

• Phonemes  SAMPA UniChar
< t ê m >
< t 6 ~ i ~ 6 ~ i ~ /
/ t ï ï /
/ t Æ ï /
< c h a m o u >
< S a m º >
/ S 6 m o /

< c h a m o u >
< S a m º >
/ S 6 m o /
< t ê m >
/ t Æ ï /

< c h a m o u >
< S a m º >
/ S 6 m o /
< t ê m >
/ t Æ ï /
Graphonemes
GOAL: to compute the most probable
pronunciation of a word given the word‟s
graphoneme form
TECHNIQUE: using n-grams

16
G2P SYSTEM
• Several errors due to incorrect stress assignment:
solidamente, incansavelmente

17
G2P SYSTEM
Marking the Vstressed improved the statistical model by
expressing graphoneme classes unequivocally
6 rules

STRESSED VOWEL ASSIGNMENT
For adverbs ending in <mente> (< pido> → <rapidamente> (fast → quickly):
• An algorithm that divides the word into two parts, <ROOT> and <mente>.
• The <ROOT> part undertakes a specific module (list of graphematic patterns which have the Vstressed
identified).
To generate a univocal graphoneme, we attributed special symbols to the Vstressed

19
To estimate the graphoneme‟s model:
• SpeechDat pronunciation dictionary
• 15k entries
• Deletion of foreign words
• Change of some transcriptions
• Standardization of the pronunciation
VOCABULARY
Applied to the CETEMPúblico vocabulary
40k words  40k pronunciations

20
CETEMPúblico 40k pronunciations:
• Iterative procedure:
• Long manual verification
• Correction of the transcriptions
• Comparison to the pronunciations of LOQUENDO
DICTIONARY
This dictionary was used for the training and test procedure.
• The majority of the transcriptions agreed.
• The transcriptions from our dictionary were the right ones most of the times.

21
EXPERIMENTS
All experiments were based on the dictionary of the
40K pronunciations:
• with stress marking
• without stress marking
Final results were obtained by evaluating the average of the five partial
results.
To train and test the model, each one of these two dictionaries was
partitioned into five folds for a cross-validation procedure.

22
The performance of the G2P conversion system was expressed
in two average error rates: average error rate of phonemes
(PER) and average error rate of words (WER)
RESULTS

23
RESULTS
The following figures summarize the results obtained using n-
grams with n between 2 and 8

24
RESULTS
The use of n-grams with large contexts (n greater than 5) did
not improve the system. In fact, there was a slight increase in
the error rates (lack of samples to estimate large contexts)

25
RESULTS
The marking of the stressed vowel contributed to a significant
improvement in the system performance

26
CONCLUSIONS
The joint-sequence model with embedded stress
assignment had good results.
By inspecting the test errors, we observed that most of them resulted
from uncommon grapheme patterns or compound words without graphic
stress marks.
The most frequent errors resulted from the pronunciation of the
stressed <e> and <o> since they could be pronounced as /E/ vs. /e/
(<selo>: verb vs. noun) and /O/ vs. /o/ (<ovos> (pl) vs. <ovo>(sing))
without any systematic rule.
Obrigada
Our system is freely available on http://www.co.it.pt/~labfala/g2p/ and
includes models, dictionaries and the G2P converter.

© 2005, it - instituto de telecomunicações. Todos os direitos reservados.
Arlindo Veiga1,2
Sara Candeias1
(saracandeias@co.it.pt)
Fernando Perdigão1,2
1Instituto de Telecomunicações, Polo de Coimbra, Portugal
2Universidade de Coimbra, DEEC, Portugal
STIL 2011
8th Symposium in Information and Human Language Technology
Oct. 14-26 2011 Cuiaba, Brazil
GENERATING A PRONUNCIATION DICTIONARY
FOR EUROPEAN PORTUGUESE
USING A JOINT-SEQUENCE MODEL
WITH EMBEDDED STRESS ASSIGNMENT

28
INTRODUCTION
Generate a Pronunciation Dictionary for PE
• Grapheme-to-Phoneme conversion (G2P)
Bom dia  b‟o~ d‟i6 (en. Good morning)
• Applications: component of ASR and TTS systems
e.g. in language learning, machine translation,…
• For correct pronunciation we need:
• G2P, stress assignment
• Contribution of this paper:
• Show phonological constraints (vowel stressed)
• Evaluate a mixed approach for G2P system
• Turn the dictionary (the model and the converter) publicly available

Candeias sti lg2p_vfinal

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