The combination of likelihood and prior modeling , HMMs , and Viterbi decoding is fundamentally the same as the standard probabilistic approaches to speech recognition ( Bahl , Jelinek , and Mercer 1983 ) and tagging ( Church 1988 ) .
a stochastic parts program and noun phrase parser for unrestricted text
Kenneth Ward Church
Dialogue Act Modeling sequence with the highest posterior probability : The combination of likelihood and prior modeling , HMMs , and Viterbi decoding is fundamentally the same as the standard probabilistic approaches to speech recognition ( Bahl , Jelinek , and Mercer 1983 ) and tagging ( Church 1988 ) . It maximizes the probability of getting the entire DA sequence correct , but it does not necessarily find the DA sequence that has the most DA labels correct ( Dermatas and Kokkinakis 1995 ) .
The combination of likelihood and prior modeling , HMMs , and Viterbi decoding is fundamentally the same as the standard probabilistic approaches to speech recognition ( Bahl , Jelinek , and Mercer 1983 ) and tagging ( @@CITATION ) .