simon king fluvmvxkmedacuk funded phd studentship available centre speech technology research university edinburgh uk wwwcstredacuk dynamic bayesian networks speech recognition hidden markov models hmms current model choice automatic speech recognition asr although seemingly simple models fact require complex system contextdependent models parameter sharing adaptation algorithms achieve best performance hmms member wider family models dynamic bayesian networks dbns infinite variety dbns waiting tried asr dbns formulated reflect understanding speech signal one example would multistreamed dbns factorial hmm factors explicit linguistic interpretations factors might represent aspects speech production process dependencies introduced hidden factors creating ever richer model structures cost increased computational complexity goal find model structures improve recognition accuracy whilst remaining computationally feasible already started exploring various forms dbn still lot scope exciting original research area toolkits compute power available work models intractable recently may techniques developed hmms transferred dbns could build things like parameter tying pronunciation variation language modeling adaptation model structure full funding fees plus living allowance eligible uk eu students available contact dr simon king information see httpwwwcstredacukopportunitiesphdhtml application information dr simon king fluvmvxkmedacuk centre speech technology research wwwcstredacuk mscphd info visit wwwhcrcedacuklanguageatedinburgh wed 06 aug 2008 053302 0000 uai funded phd studentship speech recognition using dynamic bayesian networks