Normalization of Non-Standard Words with Finite State Transducers for Russian Speech Synthesis

ANALYSIS OF IMAGES, SOCIAL NETWORKS, AND TEXTSApril, 9-11th, 2015, Yekaterinburg

Normalization of Non-Standard Words with Finite State Transducers for Russian Speech Synthesis

Artem Lukanin

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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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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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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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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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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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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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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5-9ncard 5пять

6шесть

7семь

8восемь

9девять

9

2x-9xncard2двадцать

3тридцать

4сорок

5пятьдесят

6шестьдесят

7семьдесят

8восемьдесят

9девяносто

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NUM-5-9-ncard

5­9ncard

2x­9xncard

10­19ncard

пробел

0

NUMxx­ncard

0

пробел

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units

NUM­1­ncard

NUM­2­ncard

NUM­5­9­ncard

NUM­3­4­ncard

units­1 <N:g><A:g>[ ]

units­2­4

" "

" "

из ­2­9­gcard*­1m­gcard

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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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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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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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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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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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ResultsToken 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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References1. 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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References5. 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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Normatex — Russian text normalizationgithub.com/avlukanin/normatex

Artem Lukanin

• about.me/alukanin

• @avlukanin

• [email protected]

Slides: artyom.ice-lc.com/slides/normatex

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