Pronunciation Modeling

Lecture 11

Spoken Language Processing

Prof. Andrew Rosenberg

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What is a pronunciation model?

Acoustic Model

PronunciationModel

LanguageModel

Audio Features

Phone Hypothese

Word Hypothese

Word Hypothese

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Why do we need one?

• The pronunciation model defines the mapping between sequences of phones and words.

• The acoustic model can deliver a one-best, hypothesis – “best guess”.

• From this single guess, converting to words can be done with dynamic programming alignment.

• Or viewed as a Finite State Automata.

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Simplest Pronunciation “model”

• A dictionary.• Associate a word (lexical item,

orthographic form) with a pronunciation.

ACHE EY KACHES EY K SADJUNCT AE JH AH NG K TADJUNCTS AE JH AN NG K T SADVANTAGE AH D V AE N T IH JHADVANTAGE AH D V AE N IH JHADVANTAGE AH D V AE N T AH JH

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Example of a pronunciation dictionary

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Finite State Automata view

• Each word is an automata over phones

EY K

EY K

AH D V AE N T

S

I JH

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Size of whole word models

• these models get very big, very quickly

EY K

EY K

AH D V AE N T

S

I JH

START END

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Potential problems

• Every word in the training material and test vocabulary must be in the dictionary

• The dictionary is generally written by hand• Prone to errors and inconsistencies

ACHE EY KACHES EY K SADJUNCT AE JH AH NG K TADJUNCTS AE JH AN NG K T SADVANTAGE AH D V AE N T IH JHADVANTAGE AH D V AE N IH JHADVANTAGE AH D V AE N T AH JH

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Baseforms represented by graphs

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Composition

• From the word graph, we can replace each phone by its markov model

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Automating the construction

• Do we need to write a rule for every word?

• pluralizing?– Where is it +[Z]? +[IH Z]?

• prefixes, unhappy, etc.– +[UH N]– How can you tell the difference between

“unhappy”, “unintelligent” and “under” and “

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Is every pronunciation equally likely?

• Different phonetic realizations can be weighted.

• The FSA view of the pronunciation model makes this easy.

ACAPULCO AE K AX P AH L K OWACAPULCO AA K AX P UH K OWTHE TH IYTHE TH AXPROBABLY P R AA B AX B L IYPROBABLY P R AA B L IYPROBABLY P R AA L IY

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Is every pronunciation equally likely?

• Different phonetic realizations can be weighted.

• The FSA view of the pronunciation model makes this easy.

ACAPULCO AE K AX P AH L K OW0.75ACAPULCO AA K AX P UH K OW

0.25THE TH IY

0.15THE TH AX

0.85PROBABLY P R AA B AX B L IY

0.5PROBABLY P R AA B L IY

0.4PROBABLY P R AA L IY

0.1

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Collecting pronunciations

• Collect a lot of data• Ask a phonetician to phonetically

transcribe the data.• Count how many times each

production is observed.

• This is very expensive – time consuming, finding linguists.

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Collecting pronunciations

• Start with equal likelihoods of all pronunciations

• Run the recognizer on transcribed speech– forced alignment

• See how many times the recognizer uses each pronunciation.

• Much cheaper, but less reliable

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Out of Vocabulary Words

• A major problem for Dictionary based pronunciation is out of vocabulary terms.

• If you’ve never seen a name, or new word, how do you know how to pronounce it?– Person names– Organization and Company Names– New words “truthiness”, “hypermiling”,

“woot”, “app”– Medical, scientific and technical terms

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Collecting Pronunciations from the web

• Newspapers, blog posts etc. often use new names and unknown terms.

• For example:– Flickeur (pronounced like Voyeur) randomly

retrieves images from Flickr.com and creates an infinite film with a style that can vary between stream-of-consciousness, documentary or video clip.

– Our group traveled to Peterborough (pronounced like “Pita-borough”)...

• The web can be mined for pronunciations [Riley, Jansche, Ramabhadran 2009]

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Grapheme to Phoneme Conversion

• Given a new word, how do you pronounce it.

• Grapheme is a language independent term for things like “letters”, “characters”, “kanji”, etc.

• With a phoneme to grapheme-to-phoneme converter, dictionaries can be augmented with any word.

• Some languages are more ambiguous than others.

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Grapheme to Phoneme conversion

• Goal: Learn an alignment between graphemes (letters) and phonemes (sounds)

• Find the lowest cost alignment.• Weight rules, and learn contextual variants.

T E X - T

T EH K S T

T E X T - - - - -

- - - - T EH K S T

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Grapheme to Phoneme Difficulties

• How to deal with Abbreviations– US CENSUS– NASA, scuba vs. AT&T, ASR– LOL– IEEE

• What about misspellings?– should “teh” have an entry in the dictionary?– If we’re collecting new terms from the web,

or other unreliable sources, how do we know what is a new word?

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Application of Grapheme to Phoneme Conversion

• This Pronunciation Model is used much more often in Speech Synthesis than Speech Recognition

• In Speech Recognition we’re trying to do Phoneme-to-Grapheme conversion– This is a very tricky problem.– “ghoti” -> F IH SH– “ghoti” -> silence

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Approaches to Grapheme to Phoneme conversion

• “Instance Based Learning”– Lookup based on a sliding window of 3

letters– Helps with sounds like “ch” and “sh”

• Hidden Markov Model– Observations are phones– States are letters

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Machine Learning for Grapheme to Phoneme Conversion

• Input:– A letter, and surrounding context, e.g. 2

previous and 2 following letters

• Output:– Phoneme

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Decision Trees

• Decision trees are intuitive classifiers– Classifier: supervised machine

learning, generating categorical predictions

Feature > threshold?

Class A Class B

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Decision Trees Example

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Decision Tree Training

• How does the letter “p” sound?• Training data

– P loophole, peanuts, pay, apple– F physics, telephone, graph, photo– ø apple, psycho, pterodactyl,

pneumonia

• pronunciation depends on context

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Decision Trees example

• Context: L1, L2, p, R1, R2

R1 = “h”

Yes No

P loopholeF physicsF telephoneF graphF photo

P peanutP payP appleø appleø psychoø psychoøpterodactyløpneumonia

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Decision Trees example

• Context: L1, L2, p, R1, R2

R1 = “h”Yes No

P loopholeF physicsF telephoneF graphF photo

P peanutP payP appleø appleø psychoøpterodactyløpneumonia

Yes No

Ploophole

F physicsFtelephoneF graphF photo

L1 = “o”

R1 = consonantNoYes

PpeanutP pay

P appleø psychoø pterodactylø pneumonia

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Decision Trees example

• Context: L1, L2, p, R1, R2

R1 = “h”Yes No

P loopholeF physicsF telephoneF graphF photo

P peanutP payP appleø appleø psychoøpterodactyløpneumonia

Yes No

Ploophole

F physicsFtelephoneF graphF photo

L1 = “o”

R1 = consonantNoYes

PpeanutP pay

P appleø psychoø pterodactylø pneumonia

try “PARIS”

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Decision Trees example

• Context: L1, L2, p, R1, R2

R1 = “h”Yes No

P loopholeF physicsF telephoneF graphF photo

P peanutP payP appleø appleø psychoøpterodactyløpneumonia

Yes No

Ploophole

F physicsFtelephoneF graphF photo

L1 = “o”

R1 = consonantNoYes

PpeanutP pay

P appleø psychoø pterodactylø pneumonia

Now try “GOPHER”

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Training a Decision Tree

• At each node, decide what the most useful split is.– Consider all features– Select the one that improves the performance

the most

• There are a few ways to calculate improved performance– Information Gain is typically used.– Accuracy is less common.

• Can require many evaluations

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Pronunciation Models in TTS and ASR

• In ASR, we have phone hypotheses from the acoustic model, and need word hypotheses.

• In TTS, we have the desired word, but need a corresponding phone sequence to synthesize.

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Next Class

• Language Modeling• Reading: J&M Chapter 4