Hidden Markov Model and Its Applications in Speech Recognition A Tutorial

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Transcription:

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Jg BIRS l 0/0/007 We sse he soe h odes s oegessve oe wh he followg goveg eo Cosde osevo veo e.g. fe of seeh sgl whee eh s wvefo sle. Aoegessve H 0 K L 0 + K e Rell LC Whee e Gss deede delly dsed do vles wh zeo e d ve d e he oegessve o edo oeffes. As he he df of eoes } { σ d legh of K / / σ δ πσ K e f + 0 0 δ 0 0 + K + 0 0 whee d I sho-e lyss oe eh veo wold y e de. e Jg BIRS l 0/0/007 Eh dso s hezed y ooelo veo whh defes he edo veo. I e-eso he sfoo o ooelo veo fo eh e ooe s o o vege of he ooelo veos eh weghed y he oesodg oly of eg ssoed wh he l e ooe Eh se s ssoed wh e ooes eh e ooe s defed y oegessve df: e Aoegessve H / / K e σ δ πσ whee γ γ β α β α γ Jg BIRS l 0/0/007 3 Re-eso:. Gve defe he ly fo s fo of. ze he ly fo ove d o 3. Rele wh d ee he ove l soy o s ehed. L Re-eso... E log Q Defe Q Aly fo heoe: If he. he ely s s less los eveywhee. Q Q } ˆ { ˆ Ψ Q Q Τ hs s he B-Welh e-eso hll-lg lgoh o heve L sl o he E eeo-zo lgoh. Jg BIRS l 0/0/007 4 Reeso sfoo } ˆ { ˆ Ψ Q Q Τ 0 ] [ π Fo Gss e desy H: Il se oly: Se so oly: e weghs: Σ f µ Gss ees: µ Σ µ µ

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