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Utterance segmentation made sensitive to speaker identity (features)

Utterance segmentation appears to be entirely independent of (the features used for) speaker labeling.  Specifically, it was noticed that even though the speaker labeling correctly identifies that a new speaker (very clear because it goes from male voice to female voice) after (what is transcribed as) `seven's` and before (what is transcribed as) `to` in `three sixty eight reduce speed to two one zero then descend and maintain three thousand advise seven's to one zero thousand three thousand US air six eighty six ` - that entire utterance is chunked together - this leads to errors because the 'to' should be transcribed as 'two' (and would have been had it been an utterance initial word.

  • Jan 9 2019
  • Needs review
Why is it useful?
Who would benefit from this IDEA? As a customer transcribing mulit-speaker speech, I want the transcritiption to be as accurate and correctly segmented as possible
How should it work?
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