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Language modelling (word FST)

Language modelling (word FST). Operational model for categorizing mispronunciations. prompted image = ‘circus’. step 1: decode visual image. (circus). ( c u r s us ). (circus). step 2: convert graphemes to phonemes. (/k y r s y s/). (k i r k y s ). (/s i r k y s/).

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Language modelling (word FST)

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  1. Language modelling (word FST) Operational model for categorizing mispronunciations prompted image = ‘circus’ step 1: decode visual image (circus) (cursus) (circus) step 2: convert graphemes to phonemes (/k y r s y s/) (k i r k y s) (/s i r k y s/) step 3: articulate phonemes (/k y - k y r s y s /) (/ka - i - Er - k y s/) (/s i r k y s/) spoken utterance: correct, miscue (step3)or error (steps 1, 2) SPACE symposium - 6/2/09

  2. Language modelling (word FST) Prevalence of errors of different types (Chorec data) Children with RD tend to guess more often Important to model steps 1 and 3 step 2 not so important SPACE symposium - 6/2/09

  3. s t t r a logP = -5.8 s t r t a t logP = -7.2 s t A r Creation of word FST : model step 1 correct pronunciation predictable errors (prediction model needed) SPACE symposium - 6/2/09

  4. s a t t r Es te a te Er Creation of word FST : model step 3 Per branch in previous FST Correctly articulated Restarts (fixed probabilities for now) Spelling (phonemic) (fixed probabilities for now) SPACE symposium - 6/2/09

  5. Modelling image decoding errors • Model 1 : memory model • adopted in listen project • per target word • create list of errors found in database • keep those with P(list entry = error | TW) > TH • advantages • very simple strategy • can model real words + non-real-word errors • disadvantages • cannot model unseen errors • probably low precision SPACE symposium - 6/2/09

  6. Modelling image decoding errors • Model 2 : extrapolation model (idea from ..) • look for existing words that • expected to belong to vocabulary of child (= mental lexicon) • bare good resemblance with target word • select lexicon entries from that vocabulary • feature based: expose (dis)similarities with TW • features: length differences, alignment agreement, word categories, graphemes in common, … • decision tree  P(entry = decoding error | features) • keep those with P > TH • advantage: can model not previously seen errors • disadvantage: can only model real word errors SPACE symposium - 6/2/09

  7. Modelling image decoding errors • Model 3 : rule based model (under dev.) • look for frequently observed transformations at subword level • grapheme deletions, insertions, substitutions (e.g. d  b) • grapheme inversions (e.g. leed  deel) • combinations • learn decision tree per transformation • advantages • more generic  better recall/precision compromise • can model real word + non-real word errors • disadvantage • more complex + time consuming to train SPACE symposium - 6/2/09

  8. Modelling results so far • Measures (over target words with error) • recall = nr of predicted errors / total nr of errors • precision = nr of predicted errors / nr of predictions • F-rate = 2.R.P/(R+P) • branch = average nr of predictions per word • Data : test set from Chorec database SPACE symposium - 6/2/09

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