Accuracy of HMM-based phonetic segmentation using monophone or triphone acoustic model

The paper compares the accuracy of HMM-based automatic phonetic segmentation using various signal representation same as acoustic models of various complexity, i.e. acoustic models of monophones or word-internal triphones with various number of mixtures. The precision of automatic phonetic segmentation was measured on the basis of comparison with manually segmented speech data. The analysis showed that the segmentation with acoustic models of word-internal tri-phones yielded to a better target accuracy. The best results of automatic phonetic segmentation were attained for acoustic models of word-internal triphones with four mixtures. In this case average values of shift of phone boundaries and change of phone length was about 5.9 ms and 0.2 ms respectively.