Title :
Robust parametric modeling of durations in hidden Markov models
Author :
Burshtein, David
Author_Institution :
Dept. of Electr. Eng.-Syst., Tel Aviv Univ., Israel
fDate :
5/1/1996 12:00:00 AM
Abstract :
A major weakness of conventional hidden Markov models is that they implicitly model state durations by a geometric distribution, which is usually inappropriate. This paper presents a modified Viterbi algorithm that, by incorporating proper state and word duration modeling, significantly reduces the string error rate of the conventional Viterbi algorithm for a speaker-independent, connected-digit string task. The algorithm has essentially the same computational requirements of the conventional Viterbi algorithm
Keywords :
hidden Markov models; maximum likelihood estimation; speech recognition; string matching; HMM; hidden Markov models; modified Viterbi algorithm; robust parametric modeling; speaker-independent connected-digit string task; speech recognition; state duration modeling; string error rate; word duration modeling; Error analysis; Exponential distribution; Hidden Markov models; Parametric statistics; Probability distribution; Robustness; Solid modeling; Speech recognition; State estimation; Viterbi algorithm;
Journal_Title :
Speech and Audio Processing, IEEE Transactions on
