This document discusses text normalization for Russian speech synthesis. It introduces Normatex, an open-source Russian text normalization system using finite state transducers. Normatex expands non-standard words like numbers, abbreviations, and acronyms. It achieved 84.33% recall and 93.95% precision on a test corpus. The document provides details on Normatex's normalization of numbers, acronyms, abbreviations, and its finite state transducers. Further improvements to Normatex are still underway.

1. ANALYSIS OF IMAGES, SOCIAL NETWORKS,AND TEXTS
April, 9-11th, 2015, Yekaterinburg
Normalization of Non-Standard Words
with Finite State Transducers
for Russian Speech Synthesis
Artem Lukanin

2. Text Preprocessing for Speech Synthesis
• is usually a very complex task
• Text normalization is one of the steps in text preprocessing [1]
• sentence segmentation
• tokenization
• normalization of non-standard words (NSWs)
• numbers, abbreviations, and acronyms
• different characters like % , $ , # , № , etc.
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3. Normalization of Non-Standard Words
• NSWs must be expanded into full SW to be pronounced correctly
• It's even more complex in inflective languages such as Russian
• ordinal number can be converted into 36 different word forms (6
cases * 2 numers * 3 genders)
• digit position changes the output standard word
• 1111 1 — первый
• 111 11 — одиннадцатый
• 11 1 11 — сто
• 1 1 111 — тысяча
• 11 111 — одиннадцать
тысяч
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4. Existing Russian Normalization Systems
• As a part of proprietory Text-to-Speech (TTS) systems
• Google Translate, https://translate.google.ru/
• VitalVoice, http://cards.voicefabric.ru/
• Windows SAPI voices, etc.
• As a part of open-source TTS systems
• Festival [2]
• only digit-by-digit number normalization for the Russian voice
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5. Normatex
• is the first Russian open-source normalization system, known to the
author, github.com/avlukanin/normatex
• If the input texts are normalized beforehand the quality of the
synthesized speech of existing TTS systems can be improved
• 118 finite state transducers (FSTs) for conversion of cardinal and ordinal
numbers into the corresponding numerals, which can preprocess
different ranges, time, dates, telephone numbers, postal codes, etc.
• 33 FSTs for normalization of graphic abbreviations and acronyms
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6. Test Parallel Corpus
• 66 original texts of the official site of South Ural State University,
susu.ac.ru, which contains 38,439 tokens (broad segmentation units [3]):
• 14,661 word tokens
• 333 acronyms and 98 initials; 379 graphic abbreviations
• 977 number tokens (2,511 digits)
• 66 manually preprocessed texts, where all numbers, abbreviations and
acronyms were expanded into full words or replaced with pronounceable
combination of letters
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7. Finite State Transducers
• are developed in the form of graphs in Unitex 3.1beta
• Before applying FSTs to a text, it is preprocessed:
• The text is splitted into sentences
• The text is tokenized
• Every token is assigned all possible grammatical forms
• Number FSTs are applied first to deal with numbers and measure unit
abbreviations
• Abbreviation FSTs and acronym FSTs are applied sequentially after that
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8. Cardinal Numbers
• agree with nouns in case, but the numerals один “one” and два “two”
agree in gender as well
• all the constituent words of a compound numeral agree with the
corresponding noun: двадцати одного and двадцати одной (“twenty-
one” in gen. m. and f.)
• одни (“one” in plural) agrees only with pluralia tantum, e.g. одни
ножницы “one pair of scissors”, одни брюки “one pair of pants” [4]
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9. 5-9ncard 5
пять
6
шесть
7
семь
8
восемь
9
девять
9

10. 2x-9xncard
2
двадцать
3
тридцать
4
сорок
5
пятьдесят
6
шестьдесят
7
семьдесят
8
восемьдесят
9
девяносто
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11. NUM-5-9-ncard
5­9ncard
2x­9xncard
10­19ncard
пробел
0
NUMxx­ncard
0
пробел
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12. units
NUM­1­ncard
NUM­2­ncard
NUM­5­9­ncard
NUM­3­4­ncard
units­1
<N:g>
<A:g>[ ]
units­2­4
" "
" "
из ­2­9­gcard
*
­1m­gcard
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13. Ordinal Numbers
• Simple ordinal numerals agree with nouns in gender, case and number
• In compound ordinal numerals only the last constituent word agrees with
the noun [5]: две тысячи четырнадцатом (“two thousand fourteenth” in
prepositional masculine)
• Complex ordinal numbers, ending in -00,-000,-000000,-000000000, are
written without spaces: “153000” is converted into
стопятидесятитрёхтысячный “one hundred and fifty-three thousandth”
in nominative masculine
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14. Ordinal Numbers
• Only the last constituent words -сотый “hundredth”, -тысячный
“thousandth”, -миллионный “millionth”, -миллиардный “billionth”
agree with the nouns
• The words, preceding the last word, are used in genitive plural (the
exceptions are сто “one hundred” and девяносто “ninety”, which are
used in the nominative case) [6]
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15. Acronyms
• Most acronyms should be converted into full words before speech
synthesis, because it is difficult for people to comprehend a letter-by-
letter pronunciation in speech and because acronyms are often rare for
everybody to know what phrase the acronym corresponds to
ФГБОУ ВПО «ЮУрГУ» (НИУ) → Федеральное государственное
бюджетное образовательное учреждение высшего
профессионального образования «Южно-Уральский государственный
университет» (Научно-исследовательский университет)
ФГБОУ ВПО «ЮУрГУ» (НИУ)
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16. Acronyms
• The main component of an acronym is a noun, that is why there can be
12 possible forms of the converted phrase (six cases and two numbers) in
Russian
• There are rules for all six cases in Normatex
• Acronyms can be ambiguous in different corpora
• For all ambiguous or unknown acronyms Normatex substitutes each
letter with its alphabet name: ВПП → ВэПэПэ
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17. Graphic Abbreviations
• Single interpretation: и т.д. “etc.” → и так далее , т.е. “i.e.” → то есть
• The interpretation depends on the context: и др. “et al.” → и другие
“and others”, и других “and others”, и другим “and others”, и другое
“and other”
• Ambiguous: г. → год “year”, город “city”, грамм “gram” (every noun
can have 12 word forms), Аудитория: 339-г, 339-д “Room 339-g, 339-d
• Sufficient left and right contexts should be provided in FSTs as well as
FSTs should be applied in a definite order
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18. Results
Token type Tokens Correct Errors Recall Precision
Numbers 977 920 53 94.17% 94.55%
Acronyms and initials 431 355 40 82.37% 89.87%
Graphic abbreviations 379 232 4 61.21% 98.05%
Total 1787 1507 97 84.33% 93.95%
The work is still in progress
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19. References
1. Reichel, U.D., Pfitzinger, H.R.: Text preprocessing for speech synthesis
(2006)
2. The Festival Speech Synthesis System,
http://www.cstr.ed.ac.uk/projects/festival/
3. Dutoit, T.: An introduction to text-to-speech synthesis (Vol. 3). Springer
Science & Busi-ness Media (1997)
4. Russian Grammar [Русская грамматика]. Vol. 1. Nauka, Moscow (1980)
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20. References
5. Rosental, D.E., Golub, I.B., Telenkova, M.A.: The Modern Russian Language
[Современный русский язык]. Airis-Press, Moscow (1997)
6. Rosental, D.E., Djandjakova, E.V., Kabanova, N.P.: Reference Book on
Orthography, Pronunciation, Literary Editing [Справочник по
правописанию, произношению, литературному редактированию].
CheRo, Moscow (1998)
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21. Normatex—Russian text normalization
github.com/avlukanin/normatex
Artem Lukanin
• about.me/alukanin
• @avlukanin
• artyom.lukanin@gmail.com
Slides: artyom.ice-lc.com/slides/normatex
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