Unit Selection

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! Frames
! Frames
| 756009
| 756009
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|-
 +
! mistakes (contiguous strings of incorrect phones) with left/right presense of EOW as the only context)
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| 3253
 +
|-
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! number of phonemes covered by mistakes in the corpus (coverage)
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| 27097
 +
|-
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! converage by mistakes of length 1
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| 4621
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|-
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! number of mistakes with at least 5 occurrences
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| 247
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|-
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! coverage by mistakes with at least 5 occurrences
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| 9244
|}
|}

Revision as of 04:13, 10 February 2009

Contents

Error driven unit selection

Some statistics

Size of phoneme corpus
Utterances 2000
Phonemes (including EOW and words) 95938
Frames 756009
mistakes (contiguous strings of incorrect phones) with left/right presense of EOW as the only context) 3253
number of phonemes covered by mistakes in the corpus (coverage) 27097
converage by mistakes of length 1 4621
number of mistakes with at least 5 occurrences 247
coverage by mistakes with at least 5 occurrences 9244
The probability of a particular phone occurring in the corpus, and the probability of a particular phone that is correct in the corpus

This seems roughly consistent with http://myweb.tiscali.co.uk/wordscape/wordlist/phonfreq.html.

The Confusion matrix
The confusion matrix ignoring EOW and various non-speech events

The number of triphone decision tree leaf nodes is essentially not correlated with any of {total Frames,total Phones, error Phones, error Frames} per phone. Since the units are chosen to maximally cover the mistakes in the corpus, this might suggest that deepening the triphone DTs is not the same thing as coming up with these error-based units.

Computational issues

At 5.5 minutes per utterance, decoding the entire training corpus (1,700,000 utterances) will take around 206 days on the empty cluster. That's too long. We make do with around 50000 utterances.

Syllable Units

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