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example del mo Phone Phone HMM for [m]: 0.1 0.9 0.3 0.6 0.4 C1: 0.5 C2: 0.2 C3: 0.3 C3: 0.2 C4: 0.7 C5: 0.1 C4: 0.1 C6: 0.5 C7: 0.4 Output probabilities for the phone HMM: End: Mid: Onset: FINAL 0.7 End Mid Onset 7 6 Section 15, Chapter
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dels mo unciation pron ord W sequences phone over distribution a as ed describ is rd ow Each del mo transition HMM an as resented rep Distribution 0.5 0.5 0.2 0.8 [m] [ey] [ow] [t] [aa] [t] [ah] [ow] 1.0 1.0 1.0 1.0 1.0 1. 0 =) “tomato” |] maatow tow ([ P =) “tomato” |] tow mey tow ([ P 4. 0 =) “tomato” |] tahmaatow ([ P =) “tomato” |] tow tahmey ([ P data from rned lea robabilities p transition , manually created is Structure 8 6 Section 15, Chapter
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ords w Isolated rd ow isolated r fo ) d or w|t 1: e( P do eliho lik fix dels mo rd ow + dels mo Phone ) d or w( P) d or w|t 1: e( Pα =)t 1: e| d or w( P frequencies rd ow counting yb simply obtained ) d or w( Py robabilit p r Prio define recursively: computed eb can ) d or w|t 1: e( P )t 1: e,t X( P=t 1: ` date up recursive the use and ) +1 t e,t 1: `( ard w or F = +1 t 1: ` x Σ =) d or w|t 1: e( P then and t )t x(t 1: ` accuracy 95–99% reach training with systems dictation rd o Isolated-w 9 6 Section 15, Chapter
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h eec sp uous tin Con roblems! p recognition rd o isolated-w of sequence a just Not rrelated co highly rds ow Adjacent – rds ow of sequence ely lik most 6= rds ow ely lik most of Sequence – eech sp in gaps few re a there Segmentation: – thing” “next rticulation—e.g., coa rd o Cross-w – y da do go a on accuracy 60–80% manage systems eech sp Continuous 10 6 Section 15, Chapter