Word accent patterns modelling by concatenation of mora hidden Markov models

The paper describes a new method for representing and identifying isolated word accent patterns. The word accent patterns are represented by concatenation of mora hidden Markov models for fundamental frequency feature sequences. The mora HMMs are trained by accent-related features automatically extracted without manual correction from the speech wave. These algorithms are evaluated using a speech sample set consisting of 10 speakers´ 121 words, where word accent patterns are classified by listening. All words have 4 mora and are selected from a phonetically balanced word set. Two experiments are performed to compare the automatically extracted features with the features manually corrected in unvoiced parts of the speech wave. Little difference was found in the results obtained using the two different features, indicating that the mora HMMs using automatically extracted features are useful for representing and identifying word accent patterns