Pronouncing Words in TTS Systems

1. Pronouncing Words in TTS Systems Julia Hirschberg CS 4706

2. Today • Motivation • Improve TTS intelligibility and naturalness • An application: Language Learning • Challenges for automatic word pronunciation • Standard methods • Pronunciation by rule • Pronouncing dictionaries • Innovative solutions • Pronunciation by language origin • Pronunciation by rhyming analogy • Expanding the lexicon using Active Learning techniques

3. Motivation • Intelligibility • Naturalness • Applications to language learning • Unlimited vocabulary • Type a word or phrase and hear it spoken in your target language • To imitate • To learn to recognize

4. Converting Text to Phonemes • Pronouncing numbers in different contexts • Identifying proper names • Expanding abbreviations and acronyms correctly

5. Numbers • Pronouncing numbers in different contexts • In 1996 she sold 1995 shares and deposited $42 in her 401(k). • The number is 212-555-1210. • That cc # is Visa 4444-3607-5959, expiration 2/07. • Conventions: • Years • Money • Phone numbers • Money amounts

6. Cultural Dependence • Russia: • Article 3 of the rules attached to the Moscow Telephone Network Subscribers Directory, 1916: • “Numbers over a hundred are to be pronounced as follows: 1.23—one twenty three, 9.72—nine seventy two, 70.09—seventy zero nine. In numbers over 10,000 every figure of a hundred should be pronounced separately, for example, 1.20.48—one twenty forty eight, 2.08.35—two zero eight thirty five, 3.35.29—three thirty five twenty nine, 4.49.52—four forty nine fifty two, 5.15.86—five fifteen eighty six etc., not one hundred and twenty forty eight, two hundred and eight thirty five etc.”

7. In France • A French phone number is 10 digits given in series of two: • 01-43-48-12-85 • "Zéro un, quarante-trois, quarante-huit, douze, quatre-vingt-cinq". • Numbers in addresses are always pronounced as a full number: • Chambre 823, 240 rie Rivoli • Chambre huit-cent-vingt-trois. Deux-cent-quarante, rue de Rivoli

8. Pronouncing Words • Part-of-speech: • use, close, dove, multiply, coax • Origin: • shoe (ME shoo), phoenix (Gr) • mole, attaches, resume • Morphological analysis: • ferryboat, ferryboats • Popemobile • Letter-to-sound correspondences: • oo, th, qu, e (beet, bet, bite, weigh,…)

9. Conventions for numbers and symbols: &c, evalu8, cu, tsp • Genre: email, chat, recipe, want ad, software license…

10. Word Sense Ambiguity and Pronunciation • Homographs • bass/bass • Nice/nice • desert/desert • Homograph disambiguation

11. Letter-to-Sound Rules • E.g. • I _{C}e$  /ai/ rise • Else I  /ih/ rip • Advantages • Pronounces anything, seen or not • Disadvantages • Must be built by hand • How to encode all the exceptions: • E.g. ripen/risen/riser/river

12. Proper names: • Nice, Ramirez, Ribeiro, Rise, Infiniti • Solutions • More complex rules • Exceptions dictionary • Consulted first • But how handle morphological analysis? • Rise’s hat

13. Dictionary-based Approaches • Rely on very large dictionary • Disadvantages • Hand labor to create entries • Redundancy • Cat, cats, cat’s, cats’ • Out-of-vocabulary items • Proper names: covering all U.K. surnames would require >5,000,000 entries • New words: fax, email, mudd, … • Technical terms: liposuction, anova, bernaise • Foreign borrowings: frappe, ciao, louche

14. Solutions • Morphological preprocessing before dictionary look-up • Fall back to L2Sound rules if no dictionary ‘hit’

15. More Innovative Approaches • Pronouncing OOV words • Handling proper names • Inferring country of origin: Takashita, Leroy, Kirov, Lima, Infiniti • Pronunciation by analogy • Analog/dialog • Risible/visible • Proper names: Alifano/Califano

16. Bootstrapping Phonetic Lexicons (Maskey et al ’04) • For some languages, online pronouncing lexicons exist – but for others….e.g. Nepali • How to minimize effort in creating lexicons? • Idea • Given a native speaker and a large amount of online text in the language… • Native speaker builds small lexicon by hand for seed set of N most common words in text, e.g. • is: /izh/ • the: /dhax/

17. Derive L2S rules from lexicon automatically, e.g. • is ih{zh} • the  {dh}ax … • Loop: Choose the next N most common set of words from the text and use the lexicon + L2S rules to predict pronunciations, e.g. • telephone -> /telaxfown/ • He -> /hax/? • Rise -> /rihzhax/? • Assign a confidence score to each prediction by comparing each word to all words in lexicon • If is -> /ihzh} in lexicon and no other orthographically similar words are pronounced differently, new rule his -> /hihzh/ scores high • For low confidence pronunciations, Active Learning step: • Inspect and calculate error rate • Hand correct errors and add all to lexicon

18. Build a new set of L2S rules from augmented lexicon • Iterate from Loop until performance stabilizes • Results • English: • 94% success on test set after 23 iterations, 16K entry lexicon • Performance comparable to CMUDict and 1/7 the size • German: • 90% accuracy after 13 iterations, 28K lexicon • Nepali

19. 94.6% accuracy after 16 iterations, 5K lexicon

20. Improving Pronunciation Dictionary Coverage (Fackrell and Skut ’04) • Idea: Many proper names have more than one spelling (e.g. More, Moore; Smith Smythe) • Find a mapping between OOV (Out of Vocabulary) spellings and alternate spellings – a ‘fuzzy’ match • Identify spelling alternations that are ‘pronunciation-neutral’ in an existing lexicon to produce rewrite rules for OOV words

21. How do current systems do on pronunciation? • Loquendo (temporarily unavailable) • CEPSTRAL • AT&T Naturally Speaking

22. Next Class Accenting and information status